{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Qmxwlkd7S_t"
      },
      "source": [
        "Copyright 2022 Google LLC.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IvupcJVW7d15"
      },
      "outputs": [],
      "source": [
        "#@title Default title text\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsV6P28jEZJ9"
      },
      "source": [
        "# Code excerpts from the flood forecasting inundation models\n",
        "\n",
        "These code excerpts are shared as part of the \"*Flood forecasting with machine learning models in an operational framework*\" paper, and describe the inundation modelling algorithms as were used by the flood forecasting operational framework during 2021.\n",
        "\n",
        "The first few sections of this colab define some dummy Digital Elevation Map (DEM) and some dummy inundation maps. This dummy dataset is later used as test code, and allows the readers to see the algorithmic code in action.\n",
        "\n",
        "After the definition of the dummy dataset, one can find the code for the *Thresholding Model* and the *Manifold Model* as described in the paper. For every model there is a short passage of test code that uses the dummy dataset in order to show the code in action."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NobgZSmt8jN7"
      },
      "source": [
        "# Dummy Dataset\n",
        "\n",
        "Here we define the dummy DEM and some dummy inundation maps. Note that the inundation maps match lower areas in the DEM, plus some random noise, in order to be closer to realistic scenarios. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2rwNFcxp_Y0n"
      },
      "outputs": [],
      "source": [
        "from typing import Optional, Sequence, Tuple\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import dataclasses\n",
        "\n",
        "\n",
        "# Stores the ground truth image with the corresponding gauge measurement.\n",
        "@dataclasses.dataclass\n",
        "class GroundTruthMeasurement:\n",
        "  # The ground truth inundation map.\n",
        "  ground_truth: np.ma.MaskedArray\n",
        "  # The corresponding gauge measurement.\n",
        "  gauge_measurement: float"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bo5xf1dqF00_"
      },
      "source": [
        "### Create the dummy DEM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "executionInfo": {
          "elapsed": 345,
          "status": "ok",
          "timestamp": 1646917231876,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "3DyeShHJF23l",
        "outputId": "c2ad02ef-2eaa-4366-fb8c-da51f5ea76a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.colorbar.Colorbar at 0x7f4214723210\u003e"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "DEM = np.tile(np.abs(np.arange(-16, 16, step=0.25)), (128, 1)) + 100\n",
        "DEM = DEM - np.tile(np.arange(10, 0, step=-10/128), (128, 1)).T\n",
        "DEM[32:64, 100:120] -= 10\n",
        "plt.imshow(DEM)\n",
        "plt.colorbar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5Pv1-4NGEn4"
      },
      "source": [
        "## Create the dummy inundation maps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wl50YwMsGD56"
      },
      "outputs": [],
      "source": [
        "def river_with_length(length):\n",
        "    first_row = np.zeros(128)\n",
        "    first_row[64-length:64+length] = True\n",
        "    imap = np.tile(first_row, (128, 1)).astype(bool)\n",
        "\n",
        "    # Add some random noise so things will be slightly more interesting.\n",
        "    random_mask = np.random.rand(128, 128) \u003e 0.95\n",
        "    imap ^= random_mask\n",
        "\n",
        "    return np.ma.masked_array(imap)\n",
        "\n",
        "GROUND_TRUTH = [\n",
        "    GroundTruthMeasurement(\n",
        "        ground_truth=river_with_length(3),\n",
        "        gauge_measurement=1\n",
        "    ),\n",
        "    GroundTruthMeasurement(\n",
        "        ground_truth=river_with_length(10),\n",
        "        gauge_measurement=2\n",
        "    ),\n",
        "    GroundTruthMeasurement(\n",
        "        ground_truth=river_with_length(20),\n",
        "        gauge_measurement=3\n",
        "    )\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "executionInfo": {
          "elapsed": 469,
          "status": "ok",
          "timestamp": 1646917232938,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "dXVsEdK_I_MT",
        "outputId": "21b2111a-c1ab-4653-ac78-045ee6dbb4f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for gt in GROUND_TRUTH:\n",
        "    plt.imshow(gt.ground_truth)\n",
        "    plt.title(f'For gauge level = {gt.gauge_measurement}')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMqvxmF44_-z"
      },
      "source": [
        "# Thresholding Model\n",
        "\n",
        "Below is the code for the *Thresholding model* as described in the paper."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgNKRCvtcbmb"
      },
      "source": [
        "## Thresholding model code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LwzBhd-B5D9u"
      },
      "outputs": [],
      "source": [
        "\"\"\"Library functions for predicting flood extent based on historical data.\"\"\"\n",
        "from typing import Optional, Sequence, Tuple\n",
        "\n",
        "from absl import logging\n",
        "import numpy as np\n",
        "\n",
        "# Range above heighest measurement and below lowest measurement in which we\n",
        "# expect to get measurements.\n",
        "_MEASUREMENT_BUFFER_METERS = 10\n",
        "\n",
        "# A number that is smaller than the uncertainty bound of a gauge measurement.\n",
        "_EPSILON = 0.002\n",
        "\n",
        "\n",
        "def get_cutoff_points(\n",
        "    measurements: np.ndarray,\n",
        "    precision_oriented: bool) -\u003e Tuple[Sequence[int], np.ndarray]:\n",
        "  \"\"\"Gets all valid cutoff points and threshold values.\n",
        "\n",
        "  A cutoff point is the index of a measurement at which we can separate lower\n",
        "  measurements from higher measurements. For a cutoff point i, the matching\n",
        "  threshold value must be greater or equal to measurement[i-1] and less than\n",
        "  measurement[i]. Thus, the i'th event is the first wet event.\n",
        "\n",
        "  The corresponding threshold value is a gauge measurement value which separates\n",
        "  examples up to the given cutoff from those above the given cutoff.\n",
        "\n",
        "  Args:\n",
        "    measurements: All obtained measurements in the training set, sorted in\n",
        "      ascending order.\n",
        "    precision_oriented: if true, select the i'th cutoff to be measurements[i] -\n",
        "      _EPSILON, otherwise the i'th cutoff is measurements[i-1]\n",
        "\n",
        "  Raises:\n",
        "    ValueError: If measurements are not sorted.\n",
        "\n",
        "  Returns:\n",
        "    cutoff_points: Indices at which there exists a threshold which separates\n",
        "      between positive and negative measurements. The length of the list is the\n",
        "      number of distinct measurements plus 1.\n",
        "    threshold_values: Gauge measurement values which separate at those cutoffs.\n",
        "      Shape is (num_cutoffs,).\n",
        "  \"\"\"\n",
        "  # `measurements` is sorted in ascending order. Let's just verify this.\n",
        "  if np.any(np.diff(measurements) \u003c 0):\n",
        "    raise ValueError('Expected measurements to be sorted: %r' % measurements)\n",
        "\n",
        "  # Add an artificially low threshold value, to be used for pixels which should\n",
        "  # always be wet.\n",
        "  threshold_values = [measurements[0] - _MEASUREMENT_BUFFER_METERS]\n",
        "  cutoff_points = [0]\n",
        "\n",
        "  differing_measurements = ~np.isclose(measurements[:-1], measurements[1:])\n",
        "  differing_locations = np.nonzero(differing_measurements)[0]\n",
        "  cutoff_points.extend(differing_locations + 1)\n",
        "  # For cutoffs higher than the lowest event and lower than the highest event,\n",
        "  # any threshold value between measurements[i] and measurements[i+1] can be\n",
        "  # used.\n",
        "  if precision_oriented:\n",
        "    # To be as conservative as possible, we choose measurements[i+1] - _EPSILON,\n",
        "    # in order to include as small a region as possible in the risk map.\n",
        "    threshold_values.extend(measurements[differing_locations + 1] - _EPSILON)\n",
        "  else:\n",
        "    # To alert as much as possible, we choose measurements[i],\n",
        "    # in order to include as large a region as possible in the risk map.\n",
        "    threshold_values.extend(measurements[differing_locations])\n",
        "\n",
        "  # Add the final threshold. When not precision oriented, above the highest\n",
        "  # event all pixels should be considered inundated. When precision oriented,\n",
        "  # above the inundation map should be equal to that of the highest event.\n",
        "  threshold_values.append(measurements[-1])\n",
        "  if precision_oriented:\n",
        "    threshold_values[-1] += _MEASUREMENT_BUFFER_METERS\n",
        "  cutoff_points.append(len(measurements))\n",
        "\n",
        "  logging.info('Found %d threshold values: %r', len(threshold_values),\n",
        "               np.asarray(threshold_values))\n",
        "\n",
        "  return cutoff_points, np.asarray(threshold_values, dtype=np.float)\n",
        "\n",
        "\n",
        "def _count_true_in_suffixes(imaps: np.ndarray,\n",
        "                            cutoff_points: Sequence[int]) -\u003e np.ndarray:\n",
        "  \"\"\"Returns the number of True's above the cutoff at each pixel.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width)\n",
        "      for 3d arrays, or (num_train_examples,) for 1d arrays.\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "\n",
        "  Returns:\n",
        "    Array with the same shape as imaps, where the first axis has shape\n",
        "    num_cutoffs instead of num_train_examples. For example, if imaps is an array\n",
        "    with shape (num_train_examples, height, width), the result is an array with\n",
        "    shape (num_cutoffs, height, width) whose [c,i,j] element is the number of\n",
        "    events whose pixel [i,j] is True above cutoff c.\n",
        "  \"\"\"\n",
        "  all_count_true = np.nancumsum(imaps[::-1], axis=0)[::-1]\n",
        "  # If the suffix is empty, the number of True's is zero.\n",
        "  filler_zeros = np.expand_dims(np.zeros_like(all_count_true[0]), 0)\n",
        "  all_count_true = np.concatenate([all_count_true, filler_zeros])\n",
        "  return all_count_true.take(cutoff_points, axis=0, mode='clip')\n",
        "\n",
        "\n",
        "def count_true_wets_per_cutoff(imaps: np.ndarray,\n",
        "                               cutoff_points: Sequence[int]) -\u003e np.ndarray:\n",
        "  \"\"\"Returns the number of wets observations of each pixel above each cutoff.\n",
        "\n",
        "  Wet observations of a pixel above a given cutoff can be thought of as \"true\n",
        "  wet\" decisions for that cutoff value.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width)\n",
        "      for 3d arrays, or (num_train_examples,) for 1d arrays.\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "\n",
        "  Returns:\n",
        "    Array with the same shape as imaps, where the first axis has shape\n",
        "    num_cutoffs instead of num_train_examples. For example, if imaps is an array\n",
        "    with shape (num_train_examples, height, width), the result is an array with\n",
        "    shape (num_cutoffs, height, width) whose [c,i,j] element is the number of\n",
        "    events whose pixel [i,j] is wet above cutoff c.\n",
        "  \"\"\"\n",
        "  return _count_true_in_suffixes(imaps, cutoff_points)\n",
        "\n",
        "\n",
        "def count_false_wets_per_cutoff(imaps: np.ndarray,\n",
        "                                cutoff_points: Sequence[int]) -\u003e np.ndarray:\n",
        "  \"\"\"Returns the number of dry observations of each pixel above each cutoff.\n",
        "\n",
        "  Dry observations of a pixel above a given cutoff can be thought of as \"false\n",
        "  wet\" decisions for that cutoff value.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width)\n",
        "      for 3d arrays, or (num_train_examples,) for 1d arrays.\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "\n",
        "  Returns:\n",
        "    Array with the same shape as imaps, where the first axis has shape\n",
        "    num_cutoffs instead of num_train_examples. For example, if imaps is an array\n",
        "    with shape (num_train_examples, height, width), the result is an array with\n",
        "    shape (num_cutoffs, height, width) whose [c,i,j] element is the number of\n",
        "    events whose pixel [i,j] is wet below cutoff c.\n",
        "  \"\"\"\n",
        "  not_imaps = 1 - imaps\n",
        "  return _count_true_in_suffixes(not_imaps, cutoff_points)\n",
        "\n",
        "\n",
        "def count_false_drys_per_cutoff(imaps: np.ndarray,\n",
        "                                cutoff_points: Sequence[int]) -\u003e np.ndarray:\n",
        "  \"\"\"Returns the number of wet observations of each pixel below each cutoff.\n",
        "\n",
        "  Wet observations of a pixel below a given cutoff can be thought of as \"false\n",
        "  dry\" decisions for that cutoff value.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width)\n",
        "      for 3d arrays, or (num_train_examples,) for 1d arrays.\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "\n",
        "  Returns:\n",
        "    Array with the same shape as imaps, where the first axis has shape\n",
        "    num_cutoffs instead of num_train_examples .For example, if imaps is an array\n",
        "    with shape (num_train_examples, height, width), the result is an array with\n",
        "    shape (num_cutoffs, height, width) whose [c,i,j] element is the number of\n",
        "    events whose pixel [i,j] is dry above cutoff c.\n",
        "  \"\"\"\n",
        "  all_count_false_drys = np.nancumsum(imaps, axis=0)\n",
        "  # If the cutoff is lower than all events, the number of false drys is zero.\n",
        "  filler_zeros = np.expand_dims(np.zeros_like(all_count_false_drys[0]), 0)\n",
        "  all_count_false_drys = np.concatenate([filler_zeros, all_count_false_drys])\n",
        "  return all_count_false_drys.take(cutoff_points, axis=0, mode='clip')\n",
        "\n",
        "\n",
        "def _get_pixel_threshold_index(pixel_events: np.ndarray,\n",
        "                               cutoff_points: np.ndarray,\n",
        "                               min_ratio: float) -\u003e int:\n",
        "  \"\"\"Calculates the threshold index in cutoff_points for a single pixel.\n",
        "\n",
        "  Args:\n",
        "    pixel_events: 1D array of the inundation at the pixel for all events, by\n",
        "      increasing gauge order.\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "    min_ratio: Select the threshold at each pixel such that the ratio between\n",
        "      added true positives and added false positives is above min_ratio.\n",
        "\n",
        "  Returns:\n",
        "    Index within cutoff_points of the threshold of the given pixel.\n",
        "  \"\"\"\n",
        "  while np.nansum(pixel_events):\n",
        "    true_wets = count_true_wets_per_cutoff(pixel_events, cutoff_points)\n",
        "    false_wets = count_false_wets_per_cutoff(pixel_events, cutoff_points)\n",
        "    ratios = true_wets / false_wets\n",
        "    # The empty slice, corresponding to ratios[-1], has 0 true wets and 0 false\n",
        "    # wets. We define the ratio there to be 0, as we want any slice that\n",
        "    # contain a true wet to have a higher ratio than the empty slice.\n",
        "    ratios[-1] = 0\n",
        "    # Take the last maximum. In the case we have d, nan, w, we want the\n",
        "    # threshold to be between nan and w, despite the fact that the threshold\n",
        "    # between d and nan has the same ratio.\n",
        "    best_index = ratios.shape[0] - 1 - np.nanargmax(ratios[::-1])\n",
        "\n",
        "    if ratios[best_index] \u003c min_ratio:\n",
        "      break\n",
        "    # Find the next candidate threshold on the prefix of all events below the\n",
        "    # current best cutoff point.\n",
        "    best_cutoff_point = cutoff_points[best_index]\n",
        "    pixel_events = pixel_events[:best_cutoff_point]\n",
        "    cutoff_points = cutoff_points[:best_index + 1]\n",
        "  # If the remaining slice contains only NaNs, pixel should always be\n",
        "  # inundated.\n",
        "  if np.all(np.isnan(pixel_events)):\n",
        "    return 0\n",
        "  return len(cutoff_points) - 1\n",
        "\n",
        "\n",
        "def _get_threshold_indices(imaps: np.ndarray, cutoff_points: np.ndarray,\n",
        "                           min_ratio: float) -\u003e np.ndarray:\n",
        "  \"\"\"Calculates the threshold index in cutoff_points for all pixels.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width).\n",
        "    cutoff_points: Indices into the first axis of `imaps`, at which it is\n",
        "      possible to separate between lower and higher flood events. This includes\n",
        "      all indices except those having identical gauge measurements as the\n",
        "      subsequent event.\n",
        "    min_ratio: Select the threshold at each pixel such that the ratio between\n",
        "      added true positives and added false positives is above min_ratio.\n",
        "\n",
        "  Returns:\n",
        "    threshold_indices: Array of threshold indices within cutoff_points for\n",
        "      each pixel. Shape is (height, width).\n",
        "  \"\"\"\n",
        "  threshold_indices = np.zeros_like(imaps[0, :, :], dtype=int)\n",
        "  for idx_y in range(threshold_indices.shape[0]):\n",
        "    for idx_x in range(threshold_indices.shape[1]):\n",
        "      threshold_indices[idx_y, idx_x] = _get_pixel_threshold_index(\n",
        "          imaps[:, idx_y, idx_x], cutoff_points, min_ratio)\n",
        "  return threshold_indices\n",
        "\n",
        "\n",
        "def _learn_optimal_sar_prediction_internal(imaps: np.ndarray,\n",
        "                                           measurements: np.ndarray,\n",
        "                                           min_ratio: float) -\u003e np.ndarray:\n",
        "  \"\"\"A version of learn_sar_prediction without the external data structures.\n",
        "\n",
        "  Selects the threshold such that the number of added true positives divided by\n",
        "  the number of added false positives is at least min_ratio.\n",
        "\n",
        "  Args:\n",
        "    imaps: Observed inundation maps for all training set flood events, sorted by\n",
        "      increasing gauge measurement. Shape is (num_train_examples,height,width).\n",
        "    measurements: All obtained measurements in the training set, sorted in\n",
        "      ascending order.\n",
        "    min_ratio: Selects the threshold at each pixel such that the ratio between\n",
        "      added true positives and added false positives is above min_ratio.\n",
        "\n",
        "  Returns:\n",
        "    Array of thresholds for each pixel. Shape is (height, width).\n",
        "  \"\"\"\n",
        "  cutoff_points, threshold_values = get_cutoff_points(\n",
        "      measurements, precision_oriented=True)\n",
        "\n",
        "  threshold_indices = _get_threshold_indices(imaps, np.array(cutoff_points),\n",
        "                                             min_ratio)\n",
        "  thresholds = threshold_values[threshold_indices]\n",
        "\n",
        "  if np.issubdtype(imaps.dtype, np.floating):\n",
        "    # If all inundation maps are NaN for a given pixel, make the threshold for\n",
        "    # that pixel a NaN as well. This occurs when the pixel is outside the\n",
        "    # requested forecast_region.\n",
        "    thresholds = np.where(np.all(np.isnan(imaps), axis=0), np.nan, thresholds)\n",
        "\n",
        "  return thresholds\n",
        "\n",
        "\n",
        "def masked_array_to_float_array(masked_array):\n",
        "  float_array = np.array(masked_array, dtype=float)\n",
        "  mask = np.ma.getmaskarray(masked_array)\n",
        "  float_array[mask] = np.nan\n",
        "  return float_array\n",
        "\n",
        "\n",
        "def learn_optimal_sar_prediction_from_ground_truth(\n",
        "    flood_events_train: Sequence[GroundTruthMeasurement],\n",
        "    min_ratio: Optional[float] = None) -\u003e np.ndarray:\n",
        "  \"\"\"Constructs a GaugeThresholdingModel based on historical observations.\n",
        "\n",
        "  This finds the optimal thresholding model for a specific min ratio, as\n",
        "  described in the paper.\n",
        "\n",
        "  Args:\n",
        "    flood_events_train: Sequence of GroundTruthMeasurement objects to be used\n",
        "      as training set.\n",
        "    min_ratio: float argument. If provided, selects the threshold at each pixel\n",
        "      such that the ratio between added true positives and added false positives\n",
        "      is above min_ratio.\n",
        "\n",
        "  Returns:\n",
        "    GaugeThresholdingModel proto containing the learned model. For the optimal\n",
        "    thresholding model only one threshold is learned, so the high, medium and\n",
        "    low risk thresholds of the model are identical.\n",
        "  \"\"\"\n",
        "\n",
        "  # shape=(num_train_examples, height, width)\n",
        "  imaps = np.asarray([\n",
        "      masked_array_to_float_array(fe.ground_truth) for fe in flood_events_train\n",
        "  ])\n",
        "\n",
        "  # shape=(num_train_examples,)\n",
        "  measurements = np.asarray(\n",
        "      [fe.gauge_measurement for fe in flood_events_train])\n",
        "  return _learn_optimal_sar_prediction_internal(imaps, measurements,\n",
        "                                                      min_ratio)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBJXdQWFRYcL"
      },
      "source": [
        "### Optimal min_ratio code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DFW0DkQGMDjq"
      },
      "outputs": [],
      "source": [
        "MIN_RATIOS = [0.1, 0.3, 0.5, 1, 2, 5, 10, 15, 20]\n",
        "\n",
        "class ThresholdingModel:\n",
        "\n",
        "    def f1_metric(self, ground_truths: Sequence[GroundTruthMeasurement],\n",
        "                  thresholds: np.ndarray) -\u003e float:\n",
        "        \"\"\"Returns the aggregated F1 metric for a set of thresholds.\"\"\"\n",
        "        relevant_true = predicted_true = true_positives = 0\n",
        "        for gt in ground_truths:\n",
        "            predicted = thresholds \u003c gt.gauge_measurement\n",
        "            actual = gt.ground_truth.astype(bool)\n",
        "            true_positives += np.sum(predicted \u0026 actual)\n",
        "            predicted_true += np.sum(predicted)\n",
        "            relevant_true += np.sum(actual)\n",
        "        \n",
        "        total_precision = true_positives / predicted_true\n",
        "        total_recall = true_positives / relevant_true\n",
        "        return 2/(1/total_precision + 1/total_recall)\n",
        "\n",
        "    def train(self, ground_truth: Sequence[GroundTruthMeasurement]):\n",
        "        \"\"\"Trains the model according to the provided training set.\"\"\"\n",
        "        min_ratio_to_thresholds = {}\n",
        "        f1_to_min_ratio = {}\n",
        "        for min_ratio in MIN_RATIOS:\n",
        "            min_ratio_to_thresholds[min_ratio] = (\n",
        "                learn_optimal_sar_prediction_from_ground_truth(GROUND_TRUTH, \n",
        "                                                               min_ratio))\n",
        "            f1 = self.f1_metric(ground_truth, \n",
        "                                min_ratio_to_thresholds[min_ratio])\n",
        "            print(f'For min_ratio={min_ratio} we get f1={f1}')\n",
        "            f1_to_min_ratio[f1] = min_ratio\n",
        "        \n",
        "        best_f1 = max(f1_to_min_ratio.keys())\n",
        "        best_min_ratio = f1_to_min_ratio[best_f1]\n",
        "        print('chosen min_ratio', best_min_ratio)\n",
        "        self.thresholds = min_ratio_to_thresholds[best_min_ratio]\n",
        "    \n",
        "    def infer(self, gauge_level: float):\n",
        "        \"\"\"Returns the inferred inundation model for a gauge level.\"\"\"\n",
        "        return self.thresholds \u003c gauge_level"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHcpl-9YQr_l"
      },
      "source": [
        "## Test code with dummy dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 24472,
          "status": "ok",
          "timestamp": 1646917257707,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "818wlDcRHij-",
        "outputId": "ab1b7b2d-f168-4d28-b7b8-dd5eee3ee0ea"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:83: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:292: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:292: RuntimeWarning: divide by zero encountered in true_divide\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "For min_ratio=0.1 we get f1=0.9141687514214236\n",
            "For min_ratio=0.3 we get f1=0.9141687514214236\n",
            "For min_ratio=0.5 we get f1=0.9141687514214236\n",
            "For min_ratio=1 we get f1=0.9331896551724138\n",
            "For min_ratio=2 we get f1=0.9276622825625794\n",
            "For min_ratio=5 we get f1=0.9236866063299951\n",
            "For min_ratio=10 we get f1=0.9236866063299951\n",
            "For min_ratio=15 we get f1=0.9236866063299951\n",
            "For min_ratio=20 we get f1=0.9236866063299951\n",
            "chosen min_ratio 1\n"
          ]
        }
      ],
      "source": [
        "#@title train over dummy dataset\n",
        "tm = ThresholdingModel()\n",
        "tm.train(GROUND_TRUTH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iErm58EKlZY6"
      },
      "source": [
        "### Inference examples\n",
        "\n",
        "The thresholding model has been trained on three inundation maps, corresponding to gauge levels of 1, 2 and 3. Below, one can see that the output for gauge levels of 2, 2.5, and 3.\n",
        "\n",
        "It can be seen that for 1 and 1.5, the image is less noisy. This is due to the monotinization that is done by the model, since the model does not allow for a decrease in flood where the gauge level rises.\n",
        "\n",
        "It can also be seen that the model is not able to interpolate between the images in the train set, as expected."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "executionInfo": {
          "elapsed": 1016,
          "status": "ok",
          "timestamp": 1646917258709,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "worbaaxrQzii",
        "outputId": "b7eff20e-40d9-4179-83c1-e531bd4ff0f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for gauge_level in [1, 1.5, 2]:\n",
        "    plt.title(f'Boolean image for gauge level {gauge_level}')\n",
        "    plt.imshow(tm.infer(gauge_level))\n",
        "    plt.colorbar()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYsSKSqz48wM"
      },
      "source": [
        "# Manifold Model\n",
        "\n",
        "Below is the *Manifold model* code, as descirbed in the paper. The code is split into two parts: first is the *Flood extent to depth* algorithm, which is proceeded by the *Manifold model* code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0XnEuEzzLDXI"
      },
      "outputs": [],
      "source": [
        "!apt-get -qq install -y libsm6 libxext6 \u0026\u0026 pip install -q -U opencv-python"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gtZdT2LTUBBm"
      },
      "outputs": [],
      "source": [
        "import abc\n",
        "import dataclasses\n",
        "from typing import List, Optional, Sequence, Tuple\n",
        "\n",
        "from absl import logging\n",
        "import cv2\n",
        "import numpy as np\n",
        "import scipy\n",
        "import skimage\n",
        "import skimage.measure\n",
        "import skimage.morphology"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQE8BICTSSuY"
      },
      "source": [
        "## Flood Extent to Depth code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygWjeAdDHIcp"
      },
      "outputs": [],
      "source": [
        "\"\"\"A Laplace-equation solver for water depth based on flood extent image.\n",
        "\n",
        "The module entry point is the laplace_depth_solve function, which runs the\n",
        "laplace solve algorithm and returns the output water height raster. To run it,\n",
        "one must use the LaplaceDepthSolverConfig dataclass, which holds the solver\n",
        "configurations.\n",
        "\"\"\"\n",
        "\n",
        "def down_scale(raster: np.ndarray, down_scale_factor: int, \n",
        "               fraction: float = 0.1) -\u003e np.ndarray:\n",
        "  \"\"\"Down scale a boolean raster image by down_scale_factor.\"\"\"\n",
        "  b = raster.shape[0] // down_scale_factor\n",
        "  return raster.reshape(-1, down_scale_factor, b, down_scale_factor).mean(\n",
        "      (-1, -3)) \u003e fraction\n",
        "\n",
        "\n",
        "class ForceCalculator(abc.ABC):\n",
        "  \"\"\"A generic interface for boundary conditions for a LaplaceDepthSolver.\"\"\"\n",
        "\n",
        "  @abc.abstractmethod\n",
        "  def init_inundation_map(self, inundation_map: np.ma.MaskedArray,\n",
        "                          down_scale_factor: int) -\u003e np.ndarray:\n",
        "    \"\"\"A method for per-inundation map initializations.\n",
        "\n",
        "    This function will be called by the `LaplaceDepthSolver.solve` function\n",
        "    once, and before the `calc_foreces` function will be called. It is used to\n",
        "    initialize internal data-structures that are inundation_map dependent.\n",
        "\n",
        "    Args:\n",
        "      inundation_map: The current inundation map. This inundation map will be\n",
        "        saved internally and will be used by other methods.\n",
        "      down_scale_factor: This constant implies the expected down-scale of the\n",
        "        height_raster that will be passed to `calculate_forces`.\n",
        "\n",
        "    Returns:\n",
        "      boundary_force_indices: A numpy array of shape (N, 2), where N is the\n",
        "        number of forces to be returned from the `calculate_forces` method. In\n",
        "        this array, the i'th row represents the (x, y) coordinates of the i'th\n",
        "        boundary force within the down scaled height raster.\n",
        "    \"\"\"\n",
        "    pass\n",
        "\n",
        "  @abc.abstractmethod\n",
        "  def calculate_forces(self, height_raster: np.ndarray) -\u003e np.ndarray:\n",
        "    \"\"\"A method for per-iteration calculation of the forces.\n",
        "\n",
        "    This function will be called by the `LaplaceDepthSolver.solve` function at\n",
        "    every round of the Laplace solver optimization process.\n",
        "\n",
        "    Args:\n",
        "      height_raster: The current height raster, on which the forces should be\n",
        "        applied.\n",
        "\n",
        "    Returns:\n",
        "      An array of shape N with all the forces on the height_raster passed. The\n",
        "      forces must be on a scale of the pixel-wise difference, where simply\n",
        "      adding the forces to the height raster will satisfy all the conditions.\n",
        "    \"\"\"\n",
        "    pass\n",
        "\n",
        "\n",
        "def _find_contours(inundation_map: np.ndarray) -\u003e Sequence[np.ndarray]:\n",
        "  \"\"\"Returns a list of all the contours in an inundation map.\n",
        "\n",
        "  Args:\n",
        "    inundation_map: A boolean numpy array representing an inundation map.\n",
        "\n",
        "  Returns:\n",
        "    A sequence of numpy arrays, each of shape (N_i, 2) where N_i is the length\n",
        "    of the i'th contour. Each such numpy array represents the sequence of\n",
        "    (x, y)'s of the contour. The contours are sorted from longest to shortest.\n",
        "    Note that the contours are composed of pixels from the interior of the\n",
        "    flooded region.\n",
        "  \"\"\"\n",
        "  contours, _ = cv2.findContours(inundation_map.astype(np.uint8),\n",
        "                                 mode=cv2.RETR_LIST,\n",
        "                                 method=cv2.CHAIN_APPROX_NONE)\n",
        "  contours = [contour[:, 0, ::-1] for contour in contours]\n",
        "  return sorted(contours, key=lambda c: -c.shape[0])\n",
        "\n",
        "\n",
        "def _mask_without_edge_pixels(masked_image: np.ma.MaskedArray) -\u003e np.ndarray:\n",
        "  \"\"\"Returns the image mask where the pixels on the edge are masked too.\n",
        "\n",
        "  The term \"pixels on the edge\" refers to pixels that are adjascent to either\n",
        "  a masked pixel or a pixel that is out of image bounds.\n",
        "\n",
        "  Args:\n",
        "    masked_image: The image from which the pixels on the edge should be\n",
        "      calculated.\n",
        "  \"\"\"\n",
        "  mask = skimage.morphology.dilation(np.ma.getmaskarray(masked_image))\n",
        "  mask[:, 0] = True\n",
        "  mask[0, :] = True\n",
        "  mask[:, -1] = True\n",
        "  mask[-1, :] = True\n",
        "  return mask\n",
        "\n",
        "\n",
        "class CatScanForceCalculator(ForceCalculator):\n",
        "  \"\"\"A Force calculator class based on the cat-scan algorithm.\n",
        "\n",
        "  For every flood-bounndary (contour) pixel, the force-calculator object finds\n",
        "  the water height that generates the best inundation map in the neighborhood of\n",
        "  the pixel. The forces generated by this object are the difference between the\n",
        "  current water level and the water level that will generate an inundation map\n",
        "  that is similar to the known inundation map at the neighborhood of that pixel.\n",
        "\n",
        "  For more details about the Catscan algorithm, refer to the `_catscan` function\n",
        "  documentation.\n",
        "  \"\"\"\n",
        "\n",
        "  _dem: np.ndarray\n",
        "  _local_region_width: int\n",
        "  _tolerance: int\n",
        "  _thresholds: Sequence[float]\n",
        "  _catscan_result: np.ndarray\n",
        "  _boundary_condition_min: np.ndarray\n",
        "  _boundary_condition_max: np.ndarray\n",
        "  _boundary_condition_weight: np.ndarray\n",
        "  _scaled_boundary_condition_indices: np.ndarray\n",
        "\n",
        "  # The step (in meters) between two consecutive catscan iterations.\n",
        "  _CATSCAN_RESOLUTION: float = 0.1\n",
        "\n",
        "  def __init__(self, dem: np.ndarray, local_region_width: int, tolerance: int):\n",
        "    \"\"\"Initializes the CatScanForceCalculator object.\n",
        "\n",
        "    Args:\n",
        "      dem: The DEM to use. Should be in the same scale of the ground truth\n",
        "        images.\n",
        "      local_region_width: The width (in pixels) of the region around a pixel\n",
        "        in which the DEM is compared to the inundation map.\n",
        "      tolerance: For every boundary pixel, the algorithm finds the gauge\n",
        "        interval (i.e. min/max values) that generates an inundation map that\n",
        "        best matches the local region of that pixel. The tolerance parameter\n",
        "        sets how many pixels can an inundation map deviate from the inundation\n",
        "        map that generates the best match, and still be counted good. For\n",
        "        example, for a pixel (x, y) the following are the accuracy result within\n",
        "        its local region: {69.8: 5,  69.9: 3, 70.0: 0, 70.1: 10}. If tolerance\n",
        "        is 0, then the forces will pull towards 70.0, and if tolerance is 3, the\n",
        "        forces will pull towards the interval (69.9, 70.0). For more\n",
        "        information, refer to the documentation of `_catscan`.\n",
        "    \"\"\"\n",
        "    self._dem = dem\n",
        "    self._local_region_width = local_region_width\n",
        "    self._tolerance = tolerance\n",
        "    self._boundary_condition_min = np.array([])\n",
        "    self._boundary_condition_max = np.array([])\n",
        "    self._boundary_condition_weight = np.array([])\n",
        "    self._scaled_boundary_condition_indices = np.array([[], []])\n",
        "\n",
        "  def _catscan(self, inundation_map: np.ma.MaskedArray):\n",
        "    \"\"\"Performs the Catscan algorithm.\n",
        "\n",
        "    For every possible water level, this method thresholds the DEM (i.e. creates\n",
        "    a boolean image containing the result of (dem \u003e water level)) and stores for\n",
        "    every flood boundary pixel how much is the thresholded DEM similar to the\n",
        "    flood pattern at the neighborhood area of thet pixel.\n",
        "                                                                       y\n",
        "    For example, the known inundation map at the local region       +-----+\n",
        "    of a boundary pixel at coordinates (x, y) is shown in           | ****|\n",
        "    Figure (a).                                                    x|   **|\n",
        "                                                                    |    *|\n",
        "    The Catscan algorithm thresholds the image at various           +-----+\n",
        "    different levels, and for every speicifc pixel (x, y)          Figure (a)\n",
        "    it records the difference (in pixels) of every thresholded\n",
        "    image from the known inundation map at the local region of the pixel. An\n",
        "    example can be seen in Figure (b) below.\n",
        "\n",
        "    +-------------+------------------------------------------------------------+\n",
        "    | Thresholds: |   3.0m      3.1m      3.2m      3.3m      3.4m      3.5m   |\n",
        "    +-------------+------------------------------------------------------------+\n",
        "    |             | +-----+   +-----+   +-----+   +-----+   +-----+   +-----+  |\n",
        "    | Infered     | |    *|   |   **|   |  ***|   |  ***|   |*****|   |*****|  |\n",
        "    | local       | |     |   |    *|   |    *|   |   **|   |  ***|   |*****|  |\n",
        "    | images:     | |     |   |     |   |    *|   |    *|   |  ***|   | ****|  |\n",
        "    |             | +-----+   +-----+   +-----+   +-----+   +-----+   +-----+  |\n",
        "    +-------------+------------------------------------------------------------+\n",
        "    | Difference: | 6 pixels  4 pixels  2 pixel   1 pixels  4 pixels  7 pixels |\n",
        "    +-------------+------------------------------------------------------------+\n",
        "                  Figure (b): Illustration of the Catscan algorithm\n",
        "                      for a specific pixel on the boundary.\n",
        "\n",
        "    Then, the algorithm finds for every pixel the threshold level that minimizes\n",
        "    the difference. In our example, the level 3.3m minimizes the difference for\n",
        "    pixel (x, y), and the minimum difference is 1 pixel.\n",
        "\n",
        "    In addition, the algorithm finds the thresholds that are within distance\n",
        "    of `tolerance` (parameter to the `__init__` function) pixels from the\n",
        "    minimum difference. Table (a) shows the minimum/maximum values for pixel\n",
        "    (x, y) for various values of `tolerance` configuration.\n",
        "\n",
        "               +------------+-----------------+-----------------+\n",
        "               | Tolerance  |  Pixel minimum  |  Pixel maximum  |\n",
        "               +------------+-----------------+-----------------+\n",
        "               |  0 pixels  |       3.3m      |        3.3m     |\n",
        "               |  2 piexls  |       3.2m      |        3.3m     |\n",
        "               |  4 pixels  |       3.1m      |        3.4m     |\n",
        "               +------------+-----------------+-----------------+\n",
        "                    Table (a) Illustraion of the `tolerance`\n",
        "                       configuration for the pixel (x, y).\n",
        "\n",
        "    These min/max values are stored and used to calucate the per pixel force,\n",
        "    at the `calculate_forces` method. Eventually, the force at pixel (x, y) is\n",
        "    the difference between its current level and the interval\n",
        "    [min level, max level] calculated by the catscan algorithm.\n",
        "\n",
        "    Args:\n",
        "      inundation_map: The known inundation map used by the algorithm.\n",
        "    \"\"\"\n",
        "    contours = _find_contours(np.ma.filled(inundation_map, False))\n",
        "    local_region_area = self._local_region_width**2\n",
        "\n",
        "    self.all_contours = np.concatenate(contours)\n",
        "    contour_lengths = [contour.shape[0] for contour in contours]\n",
        "    self.all_contours_start = np.cumsum([0] + contour_lengths)\n",
        "    invalid = skimage.morphology.dilation(np.ma.getmaskarray(inundation_map))\n",
        "    valid_dem_pixels = self._dem.copy()\n",
        "    valid_dem_pixels[invalid] = np.nan\n",
        "\n",
        "    # Find all the DEM values along the contours and store it in a 1D array.\n",
        "    all_dem_pixels = valid_dem_pixels[self.all_contours[:, 0],\n",
        "                                      self.all_contours[:, 1]]\n",
        "\n",
        "    # Use it to find the minimum and maximum values for the scan.\n",
        "    scan_min, scan_max = np.nanpercentile(all_dem_pixels, (1, 99))\n",
        "    scan_min -= 2 * self._CATSCAN_RESOLUTION\n",
        "    scan_max += 2 * self._CATSCAN_RESOLUTION\n",
        "    self._thresholds = np.arange(scan_min, scan_max, self._CATSCAN_RESOLUTION)\n",
        "\n",
        "    logging.info('Performing DEM catscan from %f to %f...', scan_min, scan_max)\n",
        "    contours_difference_list = []\n",
        "    for thresh in self._thresholds:\n",
        "      thresholded = self._dem \u003c thresh\n",
        "\n",
        "      # Find the per-pixel difference between the thresholded image and the\n",
        "      # inundation map image.\n",
        "      difference_map = np.logical_xor(thresholded, inundation_map)\n",
        "\n",
        "      # Use the blur function to set every pixel to be the average difference in\n",
        "      # its local region of width self._local_region_width.\n",
        "      block_difference = cv2.blur(\n",
        "          difference_map.astype(np.uint8) * local_region_area,\n",
        "          (self._local_region_width, self._local_region_width))\n",
        "\n",
        "      # Find the block differences along the contour lines.\n",
        "      contours_difference = block_difference[self.all_contours[:, 0],\n",
        "                                             self.all_contours[:, 1]]\n",
        "      contours_difference_list.append(contours_difference)\n",
        "\n",
        "    self._catscan_result = np.array(contours_difference_list)\n",
        "\n",
        "  def _get_boundary_levels(\n",
        "      self,\n",
        "      contour_index: Optional[int] = None\n",
        "  ) -\u003e Tuple[np.ndarray, np.ndarray, np.ndarray]:\n",
        "    \"\"\"Returns the min/max boundary levels for every boundary pixel.\n",
        "\n",
        "    Args:\n",
        "      contour_index: If passed, only pixels within this contour will be\n",
        "        returned.\n",
        "\n",
        "    Returns:\n",
        "      Three 1D numpy arrays, all having shape (N,) where N is the number of\n",
        "      pixels within the contour. The arrays are:\n",
        "        boundary_min: The minimal threshold for every pixel which satisfies the\n",
        "          DEM within `self._tolerance` pixels from the best matching threshold.\n",
        "        boundary_max: The maximal threshold for every pixel which satisfies the\n",
        "          DEM within `self._tolerance` pixels from the best matching threshold.\n",
        "        weight: For every boundary pixel, returns how well does it match the\n",
        "          DEM.\n",
        "    \"\"\"\n",
        "    if contour_index is not None:\n",
        "      contour_start = self.all_contours_start[contour_index]\n",
        "      contour_end = self.all_contours_start[contour_index+1] - 1\n",
        "      catscan_result = self._catscan_result[:, contour_start:contour_end]\n",
        "    else:\n",
        "      catscan_result = self._catscan_result\n",
        "\n",
        "    # Calculate the weight of every pixel in a contour according to the minimum\n",
        "    # difference between the known inundation map and the inundation map\n",
        "    # generated by thresholding the DEM, at the local region of the pixel. This\n",
        "    # number correspond to how well does the DEM matches the inundation map in\n",
        "    # the neighborhood of that pixel.\n",
        "    weight = 1 - catscan_result.min(axis=0) / (self._local_region_width**2)\n",
        "    good_region = (\n",
        "        catscan_result \u003c= catscan_result.min(axis=0) + self._tolerance)\n",
        "    good_min_index = np.argmax(good_region, axis=0)\n",
        "    good_max_index = good_region.shape[0] - np.argmax(\n",
        "        good_region[::-1, :], axis=0) - 1\n",
        "\n",
        "    boundary_min = self._thresholds[good_min_index]\n",
        "    boundary_max = self._thresholds[good_max_index]\n",
        "    return boundary_min, boundary_max, weight\n",
        "\n",
        "  def init_inundation_map(\n",
        "      self, inundation_map: np.ma.MaskedArray,\n",
        "      down_scale_factor: int) -\u003e Tuple[np.ndarray, np.ndarray]:\n",
        "    self._catscan(inundation_map)\n",
        "    min_height = np.zeros_like(self._dem)\n",
        "    max_height = np.zeros_like(self._dem)\n",
        "    weight = np.zeros_like(self._dem)\n",
        "\n",
        "    contours_min, contours_max, contours_weights = self._get_boundary_levels()\n",
        "    min_height[self.all_contours[:, 0], self.all_contours[:, 1]] = contours_min\n",
        "    max_height[self.all_contours[:, 0], self.all_contours[:, 1]] = contours_max\n",
        "    weight[self.all_contours[:, 0], self.all_contours[:, 1]] = contours_weights\n",
        "\n",
        "    boundary_condition_mask = (min_height \u003e 0)\n",
        "\n",
        "    boundary_condition_mask[_mask_without_edge_pixels(inundation_map)] = False\n",
        "    self._boundary_condition_min = min_height[boundary_condition_mask]\n",
        "    self._boundary_condition_max = max_height[boundary_condition_mask]\n",
        "    self._boundary_condition_weight = weight[boundary_condition_mask]\n",
        "    boundary_condition_indices = np.argwhere(boundary_condition_mask)\n",
        "    self._scaled_boundary_condition_indices = (\n",
        "        boundary_condition_indices // down_scale_factor)\n",
        "\n",
        "    return self._scaled_boundary_condition_indices.copy()\n",
        "\n",
        "  def calculate_forces(self, height_raster):\n",
        "    values = height_raster[self._scaled_boundary_condition_indices[:, 0],\n",
        "                           self._scaled_boundary_condition_indices[:, 1]]\n",
        "\n",
        "    down_forces = np.clip(\n",
        "        self._boundary_condition_min - values, a_min=0, a_max=None)\n",
        "    up_forces = np.clip(\n",
        "        values - self._boundary_condition_max, a_min=0, a_max=None)\n",
        "    force = (down_forces - up_forces) * self._boundary_condition_weight\n",
        "    force[np.isnan(force)] = 0.\n",
        "    return force\n",
        "\n",
        "\n",
        "@dataclasses.dataclass(frozen=True)\n",
        "class LaplaceDepthSolverConfig:\n",
        "  \"\"\"Holds the configuration for the LaplaceDepthSolver object.\n",
        "\n",
        "  Attributes:\n",
        "    down_scale_factor: The down scale factor to be used for the result water\n",
        "      height raster. For example: If the inundation map is of scale 1000x2000\n",
        "      and down_scale_factor=10, the result height raster will be of shape of\n",
        "      100x200. This factor directly controls the high frequency smoothness,\n",
        "      where the larger it is, the smoother the output is.\n",
        "    solve_iterations_factor: A factor that controls the number of iterations to\n",
        "      be done by the solver in order to reach convergence. The factor represents\n",
        "      the ratio between the length (in pixels) of the diagonal of the output\n",
        "      height raster and the number of iterations. For example: If the height\n",
        "      raster is 300x400 pixels, and solve_iterations_factor is 3 then the total\n",
        "      number of iterations will be 3 * 500 = 1500.\n",
        "    force_coeff: How strongly do the boundary forces affect the output height\n",
        "      raster. If force_coeff=1, then all the boundary conditions must be met by\n",
        "      the output height raster no matter how strong deformations are caused. If\n",
        "      force_coeff=0 then all the boundary conditions are completely ignored, and\n",
        "      the output height raster will be constant. A value within the interval\n",
        "      (0, 1) will allow different smoothness/boundary conditions tradeoff.\n",
        "    drop_iterations: How many drop iterations should be made by the solver. At\n",
        "      every drop iteration, the solver reaches convergence and produces a\n",
        "      height_raster, where between different such drop iterations, the solver\n",
        "      drops `drop_coeff` of the forces.\n",
        "    drop_coeff: Share of forces to be ignored between two consecutive drop\n",
        "      iterations. For example, if `drop_iterations`=2 and `drop_coeff`=0.05,\n",
        "      then the solver will:\n",
        "      1. Solve the equation using all the boundary forces.\n",
        "      2. Drop 5% largest forces.\n",
        "      3. Solve the equation again with the rest of the forces.\n",
        "  \"\"\"\n",
        "\n",
        "  down_scale_factor: int\n",
        "  solve_iterations_factor: float\n",
        "  force_coeff: float\n",
        "  drop_iterations: int\n",
        "  drop_coeff: float\n",
        "\n",
        "\n",
        "def shift_array(array: np.ndarray, shift: int, axis: int) -\u003e np.ndarray:\n",
        "  \"\"\"Shift a numpy array by a constant amount on a specified axis.\"\"\"\n",
        "  shifted_array = np.zeros_like(array)\n",
        "  array = np.asarray(array)\n",
        "\n",
        "  def slice_axis(start, stop):\n",
        "    return tuple(slice(start, stop) if curr_axis == axis else slice(None)\n",
        "                 for curr_axis in range(len(array.shape)))\n",
        "\n",
        "  if shift == 0:\n",
        "    return array[:]\n",
        "  elif shift \u003e 0:\n",
        "    shifted_array[slice_axis(shift, None)] = array[slice_axis(0, -shift)]\n",
        "  elif shift \u003c 0:\n",
        "    shifted_array[slice_axis(0, shift)] = array[slice_axis(-shift, None)]\n",
        "  return shifted_array\n",
        "\n",
        "\n",
        "class _LaplaceDepthSolver:\n",
        "  \"\"\"Helper class for solving the Laplace equation.\n",
        "\n",
        "  For more information, see the `laplace_depth_solve` function.\n",
        "  \"\"\"\n",
        "\n",
        "  _force_calculator: ForceCalculator\n",
        "  _neighbours: List[Tuple[int, int]]\n",
        "  _conf: LaplaceDepthSolverConfig\n",
        "  _total_neighbors: np.ndarray\n",
        "  _ignore_raster_mask: np.ndarray\n",
        "  _forces_indices: np.ndarray\n",
        "  _forces_per_pixel_raster: np.ndarray\n",
        "  _forces_per_pixel_raster: np.ndarray\n",
        "  _ignore_force_mask: np.ndarray\n",
        "\n",
        "  # During the optimization, every pixels is replaced with the average of its\n",
        "  # neighbors. This array defines the neighbors of every pixel, in (x,y) offsets\n",
        "  # from the current pixel at index (0, 0).\n",
        "  _PIXEL_NEIGHBOURS = ((0, 1), (0, -1), (1, 1), (1, -1), (0, 0))\n",
        "\n",
        "  def __init__(self, force_calculator: ForceCalculator,\n",
        "               config: LaplaceDepthSolverConfig):\n",
        "    self._force_calculator = force_calculator\n",
        "    self._config = config\n",
        "\n",
        "  def _solve_iterations(self, raster: np.ndarray) -\u003e int:\n",
        "    \"\"\"Returns the number of iterations needed for convergence.\n",
        "\n",
        "    See `LaplaceDepthSolverConfig.solve_iterations_factor` for more details.\n",
        "\n",
        "    Args:\n",
        "      raster: The raster used by the solver.\n",
        "    \"\"\"\n",
        "    diagonal_length = np.linalg.norm(raster.shape)\n",
        "    return int(self._config.solve_iterations_factor * diagonal_length)\n",
        "\n",
        "  def _init_laplace_step(self, solve_mask: np.ndarray) -\u003e None:\n",
        "    \"\"\"Initializes attributes for the `self._laplace_step` method.\"\"\"\n",
        "    self._total_neighbors = np.zeros_like(solve_mask, dtype=int)\n",
        "    for shift_axis, shift_amount in self._PIXEL_NEIGHBOURS:\n",
        "      shifted_mask = shift_array(solve_mask, shift_amount, shift_axis)\n",
        "      self._total_neighbors += shifted_mask\n",
        "\n",
        "    self._total_neighbors[self._total_neighbors == 0] = -1.\n",
        "    self._ignore_raster_mask = np.logical_not(solve_mask)\n",
        "\n",
        "  def _laplace_step(self, height_raster: np.ndarray) -\u003e np.ndarray:\n",
        "    \"\"\"Runs one Laplace gradient step.\n",
        "\n",
        "    Returns a copy of the input array, where every pixel is replaced with the\n",
        "    value of its neighbours.\n",
        "\n",
        "    Args:\n",
        "      height_raster: The input 2D array, with the same shape as `solve_mask`.\n",
        "\n",
        "    Returns:\n",
        "      A new array where every value is the average of its neighbours. The\n",
        "      neighbours are only taken from within the solve_mask.\n",
        "    \"\"\"\n",
        "    neighbours_sum = np.zeros_like(height_raster)\n",
        "    for shift_axis, shift_amount in self._PIXEL_NEIGHBOURS:\n",
        "      neighbours_sum += shift_array(height_raster, shift_amount, shift_axis)\n",
        "    return neighbours_sum / self._total_neighbors\n",
        "\n",
        "  def _init_force_step(self, inundation_map: np.ma.MaskedArray) -\u003e np.ndarray:\n",
        "    \"\"\"Initializes attributes for the `self._force_step` method.\n",
        "\n",
        "    Args:\n",
        "      inundation_map: The inundation map to initialize.\n",
        "\n",
        "    Returns:\n",
        "      The initial guess for the height_raster, in the shape of the down sampled\n",
        "      inundation map.\n",
        "    \"\"\"\n",
        "\n",
        "    # initialize the force_calculator with the inundation_map\n",
        "    self._forces_indices = self._force_calculator.init_inundation_map(\n",
        "        inundation_map, self._config.down_scale_factor)\n",
        "\n",
        "    # Set the initial guess to be the average force.\n",
        "    init_height_raster = np.ma.filled(\n",
        "        np.zeros_like(\n",
        "            down_scale(inundation_map, self._config.down_scale_factor),\n",
        "            dtype=float), 0.)\n",
        "    forces = self._force_calculator.calculate_forces(init_height_raster)\n",
        "    init_height_raster = init_height_raster + np.average(forces)\n",
        "\n",
        "    # Calculate the number of forces per (downscale) pixel.\n",
        "    self._forces_per_pixel_raster = np.zeros_like(init_height_raster,\n",
        "                                                  dtype=np.uint16)\n",
        "    np.add.at(self._forces_per_pixel_raster,\n",
        "              (self._forces_indices[:, 0], self._forces_indices[:, 1]), 1)\n",
        "\n",
        "    # Later on, the _forces_per_pixel is used as a denominator, where it is used\n",
        "    # in order to calculate the average force per down-scaled pixel. To avoid\n",
        "    # division by zero, and since the value of the average force in pixel that\n",
        "    # has no forces at all should be 0, set all the 0 values to 1.\n",
        "    self._forces_per_pixel_raster[self._forces_per_pixel_raster == 0] = 1\n",
        "\n",
        "    # Initialize the _ignore_force_mask attribute.\n",
        "    force = self._force_calculator.calculate_forces(init_height_raster)\n",
        "    self._ignore_force_mask = np.zeros_like(force, dtype=bool)\n",
        "    return init_height_raster\n",
        "\n",
        "  def _force_step(self, height_raster: np.ndarray) -\u003e np.ndarray:\n",
        "    \"\"\"Applies forces on the inundation map.\n",
        "\n",
        "    The forces are calculated by `self._force_calculator`. The forces are\n",
        "    applied at specific pixels, where the function deforms height_raster towards\n",
        "    the forces. The deformation is controlled by `self._config.force_coeff`.\n",
        "\n",
        "    Args:\n",
        "      height_raster: The input 2D array on which to apply the forces.\n",
        "\n",
        "    Returns:\n",
        "      A deformation of height_raster towards the forces.\n",
        "    \"\"\"\n",
        "    forces = self._force_calculator.calculate_forces(height_raster)\n",
        "    forces[self._ignore_force_mask] = 0.\n",
        "\n",
        "    force_raster = np.zeros_like(height_raster)\n",
        "    np.add.at(force_raster,\n",
        "              (self._forces_indices[:, 0], self._forces_indices[:, 1]),\n",
        "              forces)\n",
        "    height_raster += (\n",
        "        force_raster / self._forces_per_pixel_raster * self._config.force_coeff)\n",
        "    return height_raster\n",
        "\n",
        "  def _run_laplace_convergence(self, height_raster: np.ndarray) -\u003e np.ndarray:\n",
        "    \"\"\"Runs `self._solve_iterations()` of solve steps.\n",
        "\n",
        "    Every solve step is a combination of laplace gradient update (implemented in\n",
        "    _laplace_step method) followed by a boundary force update.\n",
        "\n",
        "    Args:\n",
        "      height_raster: The initial guess of the height raster.\n",
        "\n",
        "    Returns:\n",
        "      The result height_raster.\n",
        "    \"\"\"\n",
        "    solve_iterations = self._solve_iterations(height_raster)\n",
        "\n",
        "    for _ in range(solve_iterations):\n",
        "      height_raster[self._ignore_raster_mask] = 0.\n",
        "      height_raster = self._laplace_step(height_raster)\n",
        "      height_raster = self._force_step(height_raster)\n",
        "\n",
        "    return height_raster\n",
        "\n",
        "  def _ignore_strong_forces(self, height_raster: np.ndarray) -\u003e None:\n",
        "    \"\"\"Update self._ignore_force_mask with new strong forces.\n",
        "\n",
        "    The forces that are ignored are taken to be `self._config.drop_coeff` of all\n",
        "    the nonzero forces applied on the height raster.\n",
        "\n",
        "    Args:\n",
        "      height_raster: The height raster on which the forces are applied.\n",
        "    \"\"\"\n",
        "    force_keep = 1. - self._config.drop_coeff\n",
        "    forces = np.abs(self._force_calculator.calculate_forces(height_raster))\n",
        "    forces[self._ignore_force_mask] = 0.\n",
        "\n",
        "    nonzero_forces = forces[forces \u003e 0.0001]\n",
        "    strong_force_thresh = np.percentile(nonzero_forces, 100 * force_keep)\n",
        "    logging.info('Ignoring all forces above %f.', strong_force_thresh)\n",
        "    self._ignore_force_mask = np.logical_or(self._ignore_force_mask,\n",
        "                                            forces \u003e strong_force_thresh)\n",
        "\n",
        "  def solve(self, inundation_map: np.ma.MaskedArray) -\u003e np.ma.MaskedArray:\n",
        "    \"\"\"Given an inundation map, returns a water height raster.\n",
        "\n",
        "    Args:\n",
        "      inundation_map: The input inundation map. The inundation map is expected\n",
        "        to be a 2D boolean masked array when masked vaules represent missing\n",
        "        pixels.\n",
        "\n",
        "    Returns:\n",
        "      A 2D masked array, which has the same shape like a inundation_map, scaled\n",
        "      down by `config.down_scale_factor`. Pixels that correspond to dry area in\n",
        "      the input inundation map are masked out, where pixels that correspond to\n",
        "      wet areas hold the water height above see level.\n",
        "    \"\"\"\n",
        "    solve_mask = down_scale(\n",
        "        inundation_map.filled(False), self._config.down_scale_factor)\n",
        "    self._init_laplace_step(solve_mask)\n",
        "    init_height_raster = self._init_force_step(inundation_map)\n",
        "\n",
        "    height_raster = init_height_raster\n",
        "    for drop_iter_index in range(self._config.drop_iterations):\n",
        "      height_raster = self._run_laplace_convergence(height_raster)\n",
        "      if drop_iter_index != self._config.drop_iterations - 1:\n",
        "        self._ignore_strong_forces(height_raster)\n",
        "\n",
        "    return np.ma.masked_array(height_raster, mask=np.logical_not(solve_mask))\n",
        "\n",
        "\n",
        "def laplace_depth_solve(inundation_map: np.ma.MaskedArray,\n",
        "                        force_calculator: ForceCalculator,\n",
        "                        config: LaplaceDepthSolverConfig) -\u003e np.ma.MaskedArray:\n",
        "  \"\"\"A solver for the 2D Laplace PDE over an arbitrary region.\n",
        "\n",
        "  There are several features that this solver provides:\n",
        "  1. It supports arbitrary boundary conditions by supporting a ForceCalculator\n",
        "     object as the provider of the boundary forces.\n",
        "  2. It allows solving the equation on a down-sampled version of the image, to\n",
        "     allow for faster convergence and smoother outcome.\n",
        "  3. It allows for ignoring outlier forces.\n",
        "\n",
        "  The solver supports two different approaches for handling DEM/inundation map\n",
        "  inconsistencies:\n",
        "  1. Drop iterations: The solver supports running several drop iterations, where\n",
        "     every such iteration consists of running the solver and reaching full\n",
        "     convergence. Between two consecutive drop iterations, the solver throws\n",
        "     `config.drop_coeff` share of largest non-zero forces. This allows throwing\n",
        "     away outlier forces, and generating smoother (and more monotonic) water\n",
        "     height raster.\n",
        "  2. Boundary condition effectiveness: The solver uses the `config.force_coeff`\n",
        "     to control how strongly does the boundary condition affect the output water\n",
        "     height raster. Having force_coeff=1 will indicate that all the boundary\n",
        "     conditions must be met, no matter how strong deformations are caused. Any\n",
        "     value in the interval [0, 1) will make the solver prefer smoother output\n",
        "     water height map over satisfying all the conditions.\n",
        "\n",
        "  While both the above mechanisms serve the purpose of ignoring outlier boundary\n",
        "  conditions, each does it in a different way, and has its own advantages and\n",
        "  disadvantages. Hence, the user can decide which mechanism they prefer in the\n",
        "  specific circumstance.\n",
        "\n",
        "  This function only finds the water height within the wet pixels, where the\n",
        "  water height in dry pixels is masked out.\n",
        "\n",
        "  Args:\n",
        "    inundation_map: The input inundation map. The inundation map is expected to\n",
        "      be a 2D boolean masked array when masked values represent missing pixels.\n",
        "    force_calculator: An subclass of ForceCalculator used for boundary\n",
        "      conditions.\n",
        "    config: An instance of LaplaceDepthSolverConfig holding all the\n",
        "      configurations to be used in this run.\n",
        "\n",
        "  Returns:\n",
        "    A 2D masked array, which has the same shape like a inundation_map, scaled\n",
        "    down by `config.down_scale_factor`. Pixels that correspond to dry area in\n",
        "    the input inundation map are masked out, where pixels that correspond to wet\n",
        "    areas hold the water height above see level.\n",
        "  \"\"\"\n",
        "  lds = _LaplaceDepthSolver(force_calculator, config)\n",
        "  return lds.solve(inundation_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YKvPMqQpHn-N"
      },
      "outputs": [],
      "source": [
        "\"\"\"Utilities for the flood extent to depth algorithm.\"\"\"\n",
        "\n",
        "from typing import Tuple\n",
        "\n",
        "import cv2\n",
        "import numpy as np\n",
        "import PIL.Image\n",
        "import scipy.ndimage\n",
        "import skimage.morphology\n",
        "\n",
        "\n",
        "# The minimimum size of a flooded components to be regarded by the\n",
        "# flood_extent_to_depth algorithm. Smaller flooded components will be removed\n",
        "# from the input inundation map.\n",
        "_MIN_OBJECT_AREA_KM2 = 0.1\n",
        "\n",
        "\n",
        "def clean_inundation_map(inundation_map: np.ma.MaskedArray,\n",
        "                         scale_meters: float) -\u003e np.ma.MaskedArray:\n",
        "  \"\"\"Removes small objects and holes from inundation map.\n",
        "\n",
        "  Args:\n",
        "    inundation_map: The inundation map to process, as a boolean 2D masked array.\n",
        "    scale_meters: The inundation map scale, i.e. meters per pixel.\n",
        "\n",
        "  Returns:\n",
        "    A masked array representing the input inundation map without the small\n",
        "    objects and holes.\n",
        "  \"\"\"\n",
        "  min_object_area_pixels = 1000000 * _MIN_OBJECT_AREA_KM2 / scale_meters**2\n",
        "  filtered_inundation_map = skimage.morphology.remove_small_objects(\n",
        "      np.ma.filled(inundation_map.astype(bool), False),\n",
        "      min_size=min_object_area_pixels,\n",
        "      in_place=False)\n",
        "  skimage.morphology.remove_small_holes(\n",
        "      filtered_inundation_map,\n",
        "      area_threshold=min_object_area_pixels,\n",
        "      in_place=True)\n",
        "  return np.ma.masked_where(np.isnan(inundation_map),\n",
        "                            filtered_inundation_map)\n",
        "\n",
        "\n",
        "def _nearest_neighbhor_interpolation(\n",
        "    partial_image: np.ma.MaskedArray) -\u003e Tuple[np.ndarray, np.ndarray]:\n",
        "  \"\"\"Interpolates a partial image to a full one using nearest neighbors.\n",
        "\n",
        "  This function gets a partial 2D image, i.e. an image with missing pixels, and\n",
        "  interpolates them according to the nearest neighbour algorithm, i.e. the value\n",
        "  at a missing pixel is taken from its nearst valid pixel.\n",
        "\n",
        "  Args:\n",
        "    partial_image: A masked array, where missing pixels are masked out.\n",
        "\n",
        "  Returns:\n",
        "    full_image: The output interpolated image, that has no invalid pixels.\n",
        "    interpolation_distance_pixels: A 2D numpy array with the same shape of the\n",
        "      input image that holds for every pixel the distance (in pixels) to a\n",
        "      known pixel.\n",
        "  \"\"\"\n",
        "  edt_distances, edt_indices = scipy.ndimage.morphology.distance_transform_edt(\n",
        "      input=np.ma.getmaskarray(partial_image),\n",
        "      return_indices=True,\n",
        "      return_distances=True)\n",
        "  full_image = np.ma.getdata(partial_image)[edt_indices[0], edt_indices[1]]\n",
        "\n",
        "  return full_image, edt_distances\n",
        "\n",
        "\n",
        "def extend_height_raster(\n",
        "    partial_height_raster: np.ma.MaskedArray, down_scale_factor: int,\n",
        "    input_inundation_map: np.ma.MaskedArray,\n",
        "    inundation_map_scale: int) -\u003e Tuple[np.ndarray, np.ndarray]:\n",
        "  \"\"\"Extends a height raster to match valid pixels in the inundation map.\n",
        "\n",
        "  Args:\n",
        "    partial_height_raster: A masked array, where missing pixels are masked out.\n",
        "    down_scale_factor: The ratio between the partial_height_raster scale to the\n",
        "      input_inundation_map scale.\n",
        "    input_inundation_map: The inundation map used to generate the\n",
        "      partial_height_raster. It is used to determine which of the pixels of the\n",
        "      output height_raster are valid.\n",
        "    inundation_map_scale: The scale in meters of the input_inundation_map.\n",
        "\n",
        "  Returns:\n",
        "    height_raster: The output interpolated height raster, valid at the same\n",
        "      pixels the input_inundation_map is valid.\n",
        "    interpolation_distance: A 2D numpy array in the same shape of the\n",
        "      height_raster that holds for every pixel the distance (in meters) to a\n",
        "      known pixel.\n",
        "  \"\"\"\n",
        "\n",
        "  # interpolate the height raster to the whole image.\n",
        "  height_raster, interpolation_distance_pixels = (\n",
        "      _nearest_neighbhor_interpolation(partial_height_raster))\n",
        "\n",
        "  # Convert the interpolation_distance to be in meters rather than pixels.\n",
        "  height_raster_scale = inundation_map_scale * down_scale_factor\n",
        "  interpolation_distance = interpolation_distance_pixels * height_raster_scale\n",
        "\n",
        "  # Invalidate pixels that are in an invalid part of the inundation map. Make\n",
        "  # sure that no new invalid pixels are introduced due to the down scale by\n",
        "  # invalidating a low-res pixel only if *all* the high-res pixels are invalid.\n",
        "  fraction = 1 - 1 / down_scale_factor ** 2\n",
        "  invalid_part = down_scale(\n",
        "      np.ma.getmaskarray(input_inundation_map),\n",
        "      down_scale_factor=down_scale_factor, fraction=fraction)\n",
        "  height_raster[invalid_part] = np.nan\n",
        "\n",
        "  return height_raster, interpolation_distance\n",
        "\n",
        "\n",
        "def _keep_flooded_components(water_above_dem: np.ndarray,\n",
        "                             known_inundation_map: np.ndarray,\n",
        "                             agree_threshold: float) -\u003e np.ndarray:\n",
        "  \"\"\"Given a possible inundation map, returns only the flooded component.\n",
        "\n",
        "  Chooses only flooded compoenents from `water_above_dem` that agree with\n",
        "  `known_inundation_map`.\n",
        "\n",
        "  Args:\n",
        "    water_above_dem: A boolean map that indicates for every pixel whether its\n",
        "      calculated water height is above the DEM or below.\n",
        "    known_inundation_map: An inundation map to be used as reference. The flooded\n",
        "      components are chosen to be components that appear in this inundation map.\n",
        "    agree_threshold: A number between 0 and 1 that indicates what is the\n",
        "      minimum agreement ratio for a component to be chosen to be flooded.\n",
        "\n",
        "  Returns:\n",
        "    The output image containing only water_above_dem components that agree with\n",
        "    known_inundation_map at at least `agree_threshold` of the pixels.\n",
        "  \"\"\"\n",
        "  # water_above_dem_components indicates, for each pixel in water_above_dem,\n",
        "  # the index of the connected component to which it belongs.\n",
        "  water_above_dem_components = skimage.measure.label(\n",
        "      water_above_dem, background=0, connectivity=1)\n",
        "  num_components = np.max(water_above_dem_components) + 1\n",
        "\n",
        "  if num_components == 0:\n",
        "    return water_above_dem\n",
        "\n",
        "  component_size, bins = np.histogram(\n",
        "      water_above_dem_components,\n",
        "      bins=num_components-1,\n",
        "      range=(1, num_components))\n",
        "\n",
        "  # For each connected component, find its agree ratio with\n",
        "  # known_inundation_map.\n",
        "  agree_image = np.where(known_inundation_map, water_above_dem_components, 0)\n",
        "  components_agree, _ = np.histogram(\n",
        "      agree_image, bins=num_components-1, range=(1, num_components))\n",
        "  component_agree_ratio = dict(\n",
        "      zip(bins[:-1], components_agree / component_size))\n",
        "\n",
        "  # Keep only components that have more than agree_threshold agreement with the\n",
        "  # known_inundation_map.\n",
        "  good_components = [\n",
        "      round(c)\n",
        "      for c, ratio in component_agree_ratio.items()\n",
        "      if ratio \u003e agree_threshold\n",
        "  ]\n",
        "  return np.isin(water_above_dem_components, good_components)\n",
        "\n",
        "\n",
        "def flood_height_raster(interpolated_height_raster: np.ndarray,\n",
        "                        inundation_map: np.ma.MaskedArray, dem: np.ndarray,\n",
        "                        agree_threshold: float) -\u003e np.ndarray:\n",
        "  \"\"\"Given water height raster and a DEM, calculate the boolean inundation map.\n",
        "\n",
        "  Args:\n",
        "    interpolated_height_raster: 2D numpy array with water level in meters above\n",
        "      see level. This array can have any scale, and it will be scaled to the DEM\n",
        "      shape.\n",
        "    inundation_map: An inundation map to be used for finding the start points\n",
        "      for the flood fill algorithm. For more information, refer to\n",
        "      `_keep_flooded_components`. This array can have any scale and it will be\n",
        "      scaled to the DEM shape.\n",
        "    dem: The DEM to be used for flood, as a 2D numpy array.\n",
        "    agree_threshold: A number between 0 and 1 that indicates what is the\n",
        "      minimum agreement ratio between the output and the input inundation maps\n",
        "      for a component to be chosen to be flooded. For more information, refer to\n",
        "      `_keep_flooded_components`.\n",
        "\n",
        "  Returns:\n",
        "    The flooded inundation map, in the DEM scale.\n",
        "  \"\"\"\n",
        "\n",
        "  # Change inputs to DEM scale.\n",
        "  height_raster_upscale = PIL.Image.fromarray(\n",
        "      interpolated_height_raster).resize(dem.T.shape, \n",
        "                                         resample=PIL.Image.BILINEAR)\n",
        "  height_raster_mask_upscale = PIL.Image.fromarray(\n",
        "      np.ma.getmaskarray(inundation_map)).resize(dem.T.shape)\n",
        "  inundation_map_upscale = PIL.Image.fromarray(inundation_map).resize(\n",
        "      dem.T.shape)\n",
        "\n",
        "  # Calculate boolean inundation map.\n",
        "  water_above_dem = (height_raster_upscale \u003e dem)\n",
        "  water_above_dem[height_raster_mask_upscale] = False\n",
        "  flood_image = _keep_flooded_components(\n",
        "      water_above_dem, inundation_map_upscale, agree_threshold=agree_threshold)\n",
        "\n",
        "  depth_image = (height_raster_upscale - dem)\n",
        "  depth_image[~flood_image] = 0\n",
        "\n",
        "  overall_mask = np.logical_or(height_raster_mask_upscale, np.isnan(dem))\n",
        "  return np.ma.MaskedArray(depth_image, mask=overall_mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_vpsusw_6UFl"
      },
      "outputs": [],
      "source": [
        "def flood_extent_to_depth_solve(inundation_map: np.ma.MaskedArray, \n",
        "                                dem: np.ndarray, scale: int,\n",
        "                                laplace_config: LaplaceDepthSolverConfig,\n",
        "                                force_tolerance: float,\n",
        "                                force_local_region_width: int) -\u003e np.ndarray:\n",
        "  \"\"\"Extracts water height from flood extent image and DEM.\n",
        "\n",
        "  Args:\n",
        "    inundation_map: The inundation map as a boolean 2D array from which to\n",
        "      extract the water height. Missing values are expected to be masked out.\n",
        "    dem: The DEM to be used, as a 2D float numpy array. It's proportions must\n",
        "      match the inundation map. Invalid values are expected to be np.nan.\n",
        "    scale: The maps scale, in meters for pixel.\n",
        "    config: The solve configuration. Please refer to `LaplaceDepthSolverConfig`\n",
        "      for more details.\n",
        "    force_tolerance: see `CatScanForceCalculator.tolerance`.\n",
        "    force_local_region_width: see `CatScanForceCalculator.local_region_width`.\n",
        "\n",
        "  Returns:\n",
        "  The result water height.\n",
        "  \"\"\"\n",
        "\n",
        "  logging.info('Starting initial processing of the inundation map.')\n",
        "  processed_inundation_map = clean_inundation_map(inundation_map, scale)\n",
        "  down_scale_factor = laplace_config.down_scale_factor\n",
        "\n",
        "  force_calculator = CatScanForceCalculator(\n",
        "        dem=dem,\n",
        "        tolerance=force_tolerance,\n",
        "        local_region_width=force_local_region_width)\n",
        "  \n",
        "  logging.info('Running laplace solver on wet pixels...')\n",
        "  partial_height_raster = laplace_depth_solve(\n",
        "      inundation_map=processed_inundation_map,\n",
        "      force_calculator=force_calculator,\n",
        "      config=laplace_config)\n",
        "  \n",
        "  logging.info('Extending height raster to the entire image...')\n",
        "  height_raster, _ = extend_height_raster(\n",
        "      partial_height_raster=partial_height_raster,\n",
        "      down_scale_factor=down_scale_factor,\n",
        "      input_inundation_map=inundation_map,\n",
        "      inundation_map_scale=scale)\n",
        "\n",
        "  return height_raster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "executionInfo": {
          "elapsed": 410,
          "status": "ok",
          "timestamp": 1646917276394,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "CXICkc6pgrze",
        "outputId": "d08685ce-266b-4214-c426-3af2d4044548"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Plot flood extent to depth output\n",
        "height_raster = flood_extent_to_depth_solve(\n",
        "    inundation_map=GROUND_TRUTH[2].ground_truth, \n",
        "    dem=DEM, \n",
        "    scale=20, \n",
        "    laplace_config=LaplaceDepthSolverConfig(\n",
        "        down_scale_factor=8, \n",
        "        solve_iterations_factor=3.,\n",
        "        force_coeff=0.1,\n",
        "        drop_iterations=2,\n",
        "        drop_coeff=0.1),\n",
        "    force_tolerance=1, \n",
        "    force_local_region_width=5)\n",
        "\n",
        "plt.title('Water Height Map for inundation map 1')\n",
        "plt.imshow(height_raster)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0YKWqHLHM4PA"
      },
      "source": [
        "## The manifold model code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N59ql-gwhgro"
      },
      "outputs": [],
      "source": [
        "@dataclasses.dataclass\n",
        "class _TrainExample:\n",
        "  \"\"\"Helper class for holding per-train-example data.\"\"\"\n",
        "  height_raster: np.ndarray\n",
        "  gauge_level: float\n",
        "\n",
        "\n",
        "class ManifoldModel:\n",
        "    \n",
        "  def __init__(self, dem: np.ndarray, scale: float,\n",
        "               laplace_config: LaplaceDepthSolverConfig, \n",
        "               force_tolerance: float, \n",
        "               force_local_region_width: int,\n",
        "               flood_agree_threshold: float) -\u003e None:\n",
        "    \"\"\"Initializes a ManifoldModel object.\n",
        "\n",
        "    Args:\n",
        "      dem: A 2D array representing the DEM, in the shape of the ground truth\n",
        "        images.\n",
        "      scale: The scale of the DEM and the ground truth images.\n",
        "      laplace_config: The Laplace solver config to use.\n",
        "      force_tolerance: see `CatScanForceCalculator.tolerance`.\n",
        "      force_local_region_width: see `CatScanForceCalculator.local_region_width`.\n",
        "      agree_threshold: Used for the flood-fill algorithm. See \n",
        "        `flood_height_raster` for more details.\n",
        "    \"\"\"\n",
        "    self.dem = dem\n",
        "    self.scale = scale\n",
        "    self.laplace_config = laplace_config\n",
        "    self.force_tolerance = force_tolerance\n",
        "    self.force_local_region_width = force_local_region_width\n",
        "    self.flood_agree_threshold = flood_agree_threshold\n",
        "    self.thresholding_model = ThresholdingModel()\n",
        "\n",
        "  def train(self, ground_truth: Sequence[GroundTruthMeasurement]):\n",
        "    print(\"Training an inner thresholding model used for flood-fill.\")\n",
        "    self.thresholding_model.train(ground_truth)\n",
        "    \n",
        "    print(\"Running flood extent to depth on ground truth examples..\")\n",
        "    sorted_ground_truth = sorted(ground_truth, \n",
        "                                 key=lambda gt: gt.gauge_measurement)\n",
        "    self._train_examples = []\n",
        "    for gt in sorted_ground_truth:\n",
        "      gauge_level = gt.gauge_measurement\n",
        "      print('Running flood extent to depth algorithm for image at gauge_level',\n",
        "            gauge_level)\n",
        "      height_raster = flood_extent_to_depth_solve(gt.ground_truth,\n",
        "                                                  self.dem,\n",
        "                                                  self.scale,\n",
        "                                                  self.laplace_config,\n",
        "                                                  self.force_tolerance,\n",
        "                                                  self.force_local_region_width)\n",
        "      self._train_examples.append(_TrainExample(height_raster=height_raster,\n",
        "                                                gauge_level=gauge_level))\n",
        "      \n",
        "  def _interpolate_between(self, level: float, example_below: _TrainExample,\n",
        "                           example_above: _TrainExample) -\u003e np.ndarray:\n",
        "    \"\"\"Linearly interpolates between two train examples.\n",
        "\n",
        "    Performs the piecewise linear interpolation on the train examples.\n",
        "\n",
        "    Args:\n",
        "      level: The gauge level to be used.\n",
        "      example_below: The train example which is closest to `level` from below.\n",
        "      example_above: The train example which is closest to `level` from above.\n",
        "\n",
        "    Returns:\n",
        "      The water height raster which is the linear interpolation between the\n",
        "      provided train examples.\n",
        "    \"\"\"\n",
        "    level_below = example_below.gauge_level\n",
        "    level_above = example_above.gauge_level\n",
        "    level_ratio = (level - level_below) / (level_above - level_below)\n",
        "    return (level_ratio * example_above.height_raster +\n",
        "            (1 - level_ratio) * example_below.height_raster)\n",
        "\n",
        "  def _infer_piecewise_linear_manifold(self, gauge_level: float) -\u003e np.ndarray:\n",
        "    \"\"\"Infers piecewise linear water height manifold given gauge level.\n",
        "\n",
        "    The method performs piecewise linear interpolation between the saved train\n",
        "    examples.\n",
        "\n",
        "    Args:\n",
        "      gauge_level: The gauge level to infer for.\n",
        "\n",
        "    Returns:\n",
        "      The inferred low-resolution water height manifold.\n",
        "    \"\"\"\n",
        "    train_levels = [example.gauge_level for example in self._train_examples]\n",
        "    index = np.searchsorted(train_levels, gauge_level)\n",
        "\n",
        "    if index == 0:\n",
        "      # Lower than train event.\n",
        "      lowest_example = self._train_examples[0]\n",
        "      return lowest_example.height_raster\n",
        "\n",
        "    elif index == len(train_levels):\n",
        "      # Extreme event.\n",
        "      highest_example = self._train_examples[-1]\n",
        "      # Create a new height raster by adding the difference between the current\n",
        "      # measurement and the (previous) highest measurement to the height raster\n",
        "      # of the (previous) highest measurement.\n",
        "      return highest_example.height_raster + (gauge_level - train_levels[-1])\n",
        "    else:\n",
        "      return self._interpolate_between(\n",
        "          level=gauge_level,\n",
        "          example_below=self._train_examples[index - 1],\n",
        "          example_above=self._train_examples[index])\n",
        "\n",
        "  def infer(self, gauge_level: float):\n",
        "    reference_inundation_map = self.thresholding_model.infer(gauge_level)\n",
        "    manifold = self._infer_piecewise_linear_manifold(gauge_level)\n",
        "    return flood_height_raster(interpolated_height_raster=manifold, \n",
        "                               dem=self.dem,\n",
        "                               inundation_map=reference_inundation_map,\n",
        "                               agree_threshold=self.flood_agree_threshold)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vz1FnUWPM6s-"
      },
      "source": [
        "## Test code with dummy dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 33159,
          "status": "ok",
          "timestamp": 1646917309873,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "fxKjroEfrK2c",
        "outputId": "9baa298f-60c9-4eb6-9402-ce8c8838f5b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training an inner thresholding model used for flood-fill.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:83: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:292: RuntimeWarning: invalid value encountered in true_divide\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:292: RuntimeWarning: divide by zero encountered in true_divide\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "For min_ratio=0.1 we get f1=0.9141687514214236\n",
            "For min_ratio=0.3 we get f1=0.9141687514214236\n",
            "For min_ratio=0.5 we get f1=0.9141687514214236\n",
            "For min_ratio=1 we get f1=0.9331896551724138\n",
            "For min_ratio=2 we get f1=0.9276622825625794\n",
            "For min_ratio=5 we get f1=0.9236866063299951\n",
            "For min_ratio=10 we get f1=0.9236866063299951\n",
            "For min_ratio=15 we get f1=0.9236866063299951\n",
            "For min_ratio=20 we get f1=0.9236866063299951\n",
            "chosen min_ratio 1\n",
            "Running flood extent to depth on ground truth examples..\n",
            "Running flood extent to depth algorithm for image at gauge_level 1\n",
            "Running flood extent to depth algorithm for image at gauge_level 2\n",
            "Running flood extent to depth algorithm for image at gauge_level 3\n"
          ]
        }
      ],
      "source": [
        "m = ManifoldModel(\n",
        "    dem=DEM, \n",
        "    scale=100, \n",
        "    laplace_config=LaplaceDepthSolverConfig(\n",
        "        down_scale_factor=8, \n",
        "        solve_iterations_factor=3.,\n",
        "        force_coeff=0.9,\n",
        "        drop_iterations=1,\n",
        "        drop_coeff=0.00003),\n",
        "    force_tolerance=1, \n",
        "    force_local_region_width=5,\n",
        "    flood_agree_threshold=0.1)\n",
        "\n",
        "m.train(GROUND_TRUTH)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7q7YHIapbXvU"
      },
      "source": [
        "### Manifold interpolation example\n",
        "\n",
        "It can be seen that the manifold model is able to produce not only the flood extent, but rather the water depth at every pixel.\n",
        "\n",
        "Moreover, the model is able to interpolate between training gauge levels. Below, one can see that the manifold model was able to create a water depth image for gauge level of 1.5, which did not exist during training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "executionInfo": {
          "elapsed": 2070,
          "status": "ok",
          "timestamp": 1646917311937,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "YfPtyPgzMrCu",
        "outputId": "80ca8d73-1a1a-4528-d435-81ec4da1afc5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Ja1Lzq8xsVRuSNEbTAU3S3sC3gPea2XPSaHbTzEyqf+2OX24VwD6a7/8azuWcock84HGLmS1rYnePAoek5hfFZRNqKm8oaYAQzK4ws2/HxU9Wy7rx7+Zm9uHyz4ubvSEMY9efaWqB1cBbY2vnMcC2VMlvXFPes0JW7BJgnZl9uiYhZwCfiH+vm+o+XM6YB6/e1rqBhiVdCRxHKJpuAs4HBgDM7IvADcBJwHpgJ/C2LNttJpQeC/wV8GtJd8dlf08IZNdIOgt4GDi1iX24vPAKs55ntO5OATM7rcH7BrxrstudckAzs58y/oMXjp/qdp1z+eVPrHXOFYKZ/F5OVwATFTe9JNozQqOAj/rkCshvfepFPqaAc64gQqNAvi9kHtDc5Hkxs2fl/fFBHtDc1KWv1tXR1V1hTfJOga7wgOacy8wHSXGFIbP6xU3PmPUEMxhOPKC5IvDiZM8LRU4PaM65gvA7BZxzheDdNpxzBeJFTudcgWQZL6CbPKA55zIJrZx+L6dzrgC8Y60rhmTPLhvei6M3eZHTOVcI3srpnCsUb+V0zhWCmSh7QHNF4yM/9S4vcrrisnFeu0LyOjTXEzzH1js8oDnnCsH7oTnnCsX7oblCqS1eenGzd5hBOecPeGw6dZL6JP1S0vVxfrGkOyStl3S1pBnNJ9M5lweJKdPULa0It+cA61LznwQuMrPDga3AWS3Yh8s5+b1QhVetQytsQJO0CPhj4MtxXsDrgGvjKpcDb2pmHy6frHbEJ9cTzJRpykLSckn3x9LcuXXef6GkW2IJ8B5JJzXaZrM5tM8AHwKSOL8/8KyZleP8JmBhvQ9KWilpjaQ1www2mQzXFR7Hek6CMk2NSOoDLgZOBJYCp0laWrPaPwDXmNmRwArg8422O+WAJumNwGYzu2sqnzezVWa2zMyWDTBzqslwznWIWUvr0I4C1pvZBjMbAq4CTqndJbBPfL0v8FijjTbTynkscHLMBs6KO/4ssJ+k/phLWwQ82sQ+nHO5ISrZWzkXSFqTml9lZqtS8wuBjan5TcDRNdv4GPADSe8G5gAnNNrplHNoZnaemS0ys8MI2cEfmdnpwC3Am+NqZwDXTXUfLmcmGpOzzjPTXPFMog5tS7UEFqdVjbZdx2nAZWa2CDgJ+JqkCWNWOzqVfBh4v6T1hDq1S9qwD5cj3hetN1Tv5WxRkfNR4JDUfL3S3FnANQBm9nNCSXDBRBttScdaM7sVuDW+3kAoH7ui8gDWm6ylDdp3AkskLSYEshXAW2rWeQQ4HrhM0u8SAtpTE23U7xRwzmXWqlufzKws6WzgRqAPuNTM7pN0AbDGzFYDHwD+VdL7CJfRM80mDqke0JxzmdjkGgUab8/sBuCGmmUfTb1eS2h8zMwDmssmabyKK76896H2gOacyyzrXQDd4gHNOZeJmQc0VwSWKm/WKXKYgFK+T3TXGv6AR1cY8nq0nud1aK4Qah8PlPcT27WeIZKcP+DRA5pzLrO8X8c8oDnnsvFGATftWeI3nrtROT8VPKA55zLzHJorhnQrQM6v0q49DEgSD2iuKAwPZr3MiJ0O88sDmmvIzHI+vKzrlLx31/GA5pzLzgOaK4R6l+acFz9cq2Ufoq5bPKC5xpJwz5MPJuw8h+YKR5b789q1g4F5K6crhNi51gdE6XUe0FwR5bwuxbVJzi9oHtBcY9W6s5yfzK4Dcn4OeEBzLWHyHFvhecdaVxieS3N4x1pXBEnNo2pzfpV2bZTzVs6mHj8paT9J10r6jaR1kl4tab6kmyQ9EP/Oa1ViXZfl/Ors2k+WbeqWZp+n+1ng+2b2MuD3gXXAucDNZrYEuDnOu+ku72UN1342ialLphzQJO0L/CFwCYCZDZnZs8ApwOVxtcuBNzWbSJcDE1X6e4NAj1CobsgydUkzObTFwFPAVyT9UtKXJc0BDjSzx+M6TwAH1vuwpJWS1khaM8xgE8lwHVM9T9NlCo9lvaWoOTRCg8KrgC+Y2ZHADmqKl2Y27tczs1VmtszMlg0ws4lkuLYrlUZzYemgFgObCQ9svSLJOHVJMwFtE7DJzO6I89cSAtyTkg4CiH83N5dElxupEkU1vnmDZw+p9kNrUZFT0nJJ90taL6luXbukUyWtlXSfpG802uaUA5qZPQFslPTSuOh4YC2wGjgjLjsDuG6q+3A5IUFJe3aeVeqv5HVpPaBVrZyS+oCLgROBpcBpkpbWrLMEOA841sxeDry30Xab7Yf2buAKSTOADcDbCEHyGklnAQ8Dpza5D9dttUVOgWRjipmeU+sRrasfOwpYb2YbACRdRWhQXJta5x3AxWa2FcDMGpb2mgpoZnY3sKzOW8c3s12XU6r5G197MHN1LJC0JjW/ysxWpeYXAhtT85uAo2u28RIAST8D+oCPmdn3J9qp3yngGlIsTlqJkWAmMVq2qBY5XeFNotPsFjOrl9mZjH5gCXAcsAi4TdIrYvewuprtWOt6zGijQKqF0/UGI9z6lGVq7FHgkNT8orgsbROw2syGzexB4LeEADcuD2huYiqNNgjUFDnHtHT6mdQbWtcP7U5giaTFsQ5+BaFBMe27hNwZkhYQiqAbJtqon4auMZVGz5SRYFZt0upaqlwXtKqV08zKwNnAjYRbJq8xs/skXSDp5LjajcDTktYCtwAfNLOnJ9qu16G5zKy2c22V/HloPaOFdwGY2Q3ADTXLPpp6bcD745SJBzTXWCn2MUu1aKp6d4DHsd6S82cUeEBzk6M9z+kx9WuusLr9aKAsPKC5xurcBeCtnD0q5w949IDmpqQ6gna4avtAnb3Cc2jOueLwgOYKJd1TI/1YNH+ibfF5HZorjPECVpcf6Oc6LOe/tQc0NyWeIetN6uLDG7PwOwVc02R09SmlzlV5Ds1NndXplOaKLee/twc0l52N/rV0BzTDy6C9wBsFnHOF4gHNTXup3Ff6Cm3T4IrtWiznv7cHNNe06XCPn2ueyH8rpwc0NyUjdWgeyHrHNLhweUBzkzfSOJBuGDBvGOgFOf+JPaC55uX8JHctlPPf2gOay8yfquG8yOmKwYuTDnJ/QfOA5qbEUjely29Q7w2W/1bOpu7llPQ+SfdJulfSlZJmxWGp7pC0XtLVcYgqN50lY6NV3osdro1aN4xdW0w5oElaCLwHWGZmRxCGal8BfBK4yMwOB7YCZ7UioS4fPJj1tlYNY9cuzT5tox/YS1I/MBt4HHgdcG18/3LgTU3uw+VBwpgrb/oR3K6HFDWHZmaPAp8CHiEEsm3AXcCzcRBRCEO5L6z3eUkrJa2RtGaYwakmw3XTSH808yfW9oKswWw6BjRJ84BTgMXAwcAcYHnWz5vZKjNbZmbLBpg51WS4bvDY1ZNE/ouczbRyngA8aGZPAUj6NnAssJ+k/phLWwQ82nwyXVdZqmlrj0E5O5oS12V5r2Jopg7tEeAYSbMlCTgeWAvcArw5rnMGcF1zSXR5kW6y9xJmjypqkdPM7iBU/v8H8Ou4rVXAh4H3S1oP7A9c0oJ0ui6rW0eWbhhIPML1hBYGNEnLJd0fu3idO8F6fybJJC1rtM2mOtaa2fnA+TWLNwBHNbNdl3Meu3pTC+vHJPUBFwOvJzQe3ilptZmtrVlvLnAOcEeW7fogKW7yqg/ZyHuFimu91uXQjgLWm9kGMxsCriI0Mtb6J0Lf1t1ZNuoBzWViEpY6W5R+cpCAkvb4jCseJdmmDBYCG1Pze3TxkvQq4BAz+/es6fN7OV1jKo1e+kQqh1adPJj1iklkyhdIWpOaX2VmqzLvRyoBnwbOzLxHPKC5LKq5L40+01HxzB55cK0HteKbXAvmFjObqBL/UeCQ1HxtF6+5wBHAraETBS8AVks62czSgXIMD2gum1TAMsUYp9F5PJ71htZVm94JLJG0mBDIVgBvGdmN2TZgQXVe0q3A300UzMDr0FwWMZiZVFPkrD4/qCupch3WyjsFYsf7s4EbgXXANWZ2n6QLJJ081TR6Ds1lE4NZbZFzNLjJ69J6gFrY39DMbgBuqFn20XHWPS7LNj2gucZUGltHphDQlCpymsey4psGD/L0gOYyq9aVWe0y1zPy3vXQA5qbkEqxBaDEmLoyjVSo4F03eokHNDfdqaa4WS1yjmnldD3Bc2iuGKT6dWXVeW8v7w0e0Ny0V23BrObURhoFbLRezYucxWf5H/XJA5rLzh+C1tOq1aZ55gHNNWa2RzAzE2YaGZPTxxToETn/nT2guYYsBjRVixxJHPXJwmsZUMn3ie5aw3NoblqzxMLTaKuPhYmNm0kSWgiqQS7vJ7prAe9Y6wohqaAkGa0UTkJAs4pSy/Yslrri8UYBN/0l1SKnjdx8XC1yhjo0H5ezV3hAc9OfJahiqEIYQT0BSwSJRp9S6nVoxRcvXnnmAc1NzJLR4mTq8TBJulEgAZIkTK7Q8l5X6gHNNWTV4mYSg1qiUDdcbRSw0GjgeoAHNDftVSpQGdsoYMlog4AqhEYDV2jesdYVQ5KM9EMbbQionXJ+prvmVXPpOeYBzU3MDKsklCoJSuIJnQhLhEYaBQxVjMSDWvHl/Cdu+IwESZdK2izp3tSy+ZJukvRA/DsvLpekz8Wh3e+J4+q56c5CDo0k1W0j0UiDQOhwm/Mz3bVEq8YUaJcsD325DFhes+xc4GYzWwLcHOcBTgSWxGkl8IXWJNN1k1X7oaXuFhh5llC1uFnxOrTCM+JdIxmmLmkY0MzsNuCZmsWnAJfH15cDb0ot/6oFtwP7STqoVYl1XVSbA0vVpVU717oeULf+tM7UJVN9LN+BZvZ4fP0EcGB83XB49ypJKyWtkbRmmMEpJsM510l5L3I23ShgZiZN/ivEYeFXAeyj+X55z7uRsTmr86lBU3xMgZ6R91bOqebQnqwWJePfzXF5o+Hd3TSkUvWJtdUF7HkZLnlAK7ysxc1pWORcDZwRX58BXJda/tbY2nkMsC1VNHXTlcJpUs2RjS43H/Gph4TrmGWauqVhkVPSlcBxwAJJm4DzgU8A10g6C3gYODWufgNwErAe2Am8rQ1pdt2QKnJadXgBjc57UOsROW/MbhjQzOy0cd46vs66Bryr2US5nKkWJycqVnpA6wl5f0yU3yngJlbNmZU0WuTcY5Dh7iXPddA0eGKtj6boGlJ6CDsYE8h8GLteYiO3vzWaspC0XNL98c6ic+u8/35Ja+NdRzdLOrTRNj2guewkLA44HMbktNFgVqoZYd0VU3UEsEZTA5L6gIsJdxctBU6TtLRmtV8Cy8zs94BrgX9utF0PaK6xUmnMIMOuR1UfF5VhyuAoYL2ZbTCzIeAqwp1Go7szu8XMdsbZ2wndwCbkAc01Fouc6U61e04e6XpC9hzaguqdQHFaWbOlzHcVRWcB32uUPG8UcNmlAtiYbhslPKD1iuyNAlvMbFkrdinpL4FlwGsbresBzTVWKvmdAA5o6ZOJM91VJOkE4CPAa82s4U3fXuR0jSVJVx8J43LCGBn1q+HU2J3AEkmLJc0AVhDuNBoh6UjgS8DJZra5zjb24Dk0l52N/rXUo4P8AY+9QbTutiYzK0s6G7gR6AMuNbP7JF0ArDGz1cC/AHsD34wt6I+Y2ckTbdcDmmss3RQ/7g3JHtB6Qgt/ZzO7gXC7ZHrZR1OvT5jsNj2guWxqBkkxExoZPd1yf4+fa5GcX7g8oLnGqhXBDU5my/nJ7ppUrUPLMQ9orqGRgYYtPuBvZCyB0XE6lSR5v83PtUDex1/1gOYmFnNdY+7PGxkkpTtJct2S7bambvKA5hqLoz6NGcau9hnyOT/RXQtMg9/ZA5przEIxY+yI6TXD2Hk/td6Q7xKnBzSXkdkeRUwfxq73+AMe3bQ3MtBwtesGhCv1SJcN81xar/CA5qY9S5Uzasdd9Bxa7zCDSr7LnB7QXGMjuTDG1KEpSRU7PXfWG3J+4fKA5rJJ90Wrue1JGZ9S6gog57+zBzSXTfX2pmogS0Ir52hn23yf6K4FjNzXk3pAc9kkCUqMUqX6dI34uOUKUDEoV8bWtbkCstz/xh7QXHaxHm0koKXrzzyHVnyGNwq4gjBDlZBLIxGqCBJijs2wcrnbKXSdkPMLV8Mn1kq6VNJmSfemlv2LpN/E8fK+I2m/1HvnxWq+masAAAcHSURBVHH27pf0hnYl3HWWjdycPrbIiYEqoRXUcl6/4lqgRcPYtUuWR3BfBiyvWXYTcEQcL++3wHkAcVy9FcDL42c+H8ffc9NdYrEerRrUYreNuJyk0u0UurbLGMzyHNDM7DbgmZplPzCzahkjPV7eKcBVZjZoZg8C6wnj77kCUCU1MnYMaqVKzKFVPKAVnhEvXhmmLmnFIClvZ3S8vMxj7UlaWR2zb5iGg7m4brNwoo42BJDqwpFglST3LWCuBXKeQ2uqUUDSR4AycMVkP2tmq4BVAPtovle+5J1ZzKFVGwKEKlCqhOXmObQeUOBbnySdCbwRON5Gn72caaw9N01ZLG4aNZ1sEw9ovcDAcp4Ln1KRU9Jy4EOE8fJ2pt5aDayQNFPSYmAJ8Ivmk+m6LglXZ8WOtWNaPCtJqEPLeZO+a4HEsk1d0jCHJulK4DhggaRNwPmEVs2ZwE1xvLzbzeydcVy9a4C1hKLou8zML91FUK1DS2y0pdNGWzk9h9Yjcn7RahjQzOy0OosvmWD9C4ELm0mUy6EkdKwdOzBKuPUpLM/3ie5awKyrLZhZ+J0CLhMrl9Gu3fTvqtA32EffYIm+3dC/qxLu43S9IecXLg9oLhOrVLChIUpDCX3DRqkMpWGjNNTdfkeuk/Lfmu0BzWVig4NUBgfpf/YAZszqY2B7HzO2G/3bdmGD3o+wJ/jjg1zhDA7Rt6tM3+4Z9O82tHMQhoa7nSrXKUXstuF6l3YP0ff8IAM7jf4dCWzfgQ0NdTtZrgOMMGBOlikLScvjQyzWSzq3zvszJV0d379D0mGNtukBzU2K7dhJadsOZmxPGNg+TPLsNhLPofUGiw94zDI1EB9acTFwIrAUOC0+3CLtLGCrmR0OXAR8stF2PaC5SbGhIRgcojSc0DdYCfVn/qSNnmGVSqYpg6OA9Wa2wcyGgKsID7dIOwW4PL6+FjhesePreHJRh7adrVt+aNfuALZ0Oy3AAjwdaWPT8XycHu9yOrpnOqfj0GZ2uJ2tN/7Qrl2QcfVZktak5lfF+7er6j3I4uiabYysY2ZlSduA/Znge+cioJnZAZLWmNmybqfF0+Hp8HTUZ2a1z0XMHS9yOue6IcuDLEbWkdQP7As8PdFGPaA557rhTmCJpMWSZhCedL26Zp3VwBnx9ZuBH6We7FNXLoqc0arGq3SEp2MsT8dYno4WiHViZwM3An3ApfHhFhcAa8xsNeGe8a9JWk94avaKRttVg4DnnHPThhc5nXOF4QHNOVcYuQhojW6BaNM+D5F0i6S1ku6TdE5cPl/STZIeiH/ndSg9fZJ+Ken6OL843u6xPt7+MaMDadhP0rVxzNV1kl7djeMh6X3xN7lX0pWSZnXqeIwzDm3dY6DgczFN90h6VZvT4ePhNtD1gJbxFoh2KAMfMLOlwDHAu+J+zwVuNrMlwM1xvhPOAdal5j8JXBRv+9hKuA2k3T4LfN/MXgb8fkxPR4+HpIXAe4BlZnYEocJ4BZ07Hpex5zi04x2DEwmPmV8CrAS+0OZ0+Hi4jZhZVyfg1cCNqfnzgPO6kI7rgNcD9wMHxWUHAfd3YN+LCP8orwOuB0ToDd1f7xi1KQ37Ag8SG4pSyzt6PBjtHT6f0Ap/PfCGTh4P4DDg3kbHAPgScFq99dqRjpr3/hS4Ir4e8z9DaDl8dTt/p7xOXc+hMYmxPNsl3sV/JHAHcKCZVW/seQI4sANJ+Axh0JnqXb37A8/a6GDOnTgmi4GngK/Eou+XJc2hw8fDzB4FPgU8QrjBahtwF50/HmnjHYNunrtTGg+36PIQ0LpK0t7At4D3mtlz6fcsXO7a2q9F0huBzWZ2Vzv3k0E/8CrgC2Z2JLCDmuJlh47HPMJNyYuBg4E57Fn06ppOHINGmhkPt+jyENC6NpanpAFCMLvCzL4dFz8p6aD4/kHA5jYn41jgZEkPEZ448DpCXdZ+8XYP6Mwx2QRsMrM74vy1hADX6eNxAvCgmT1lZsPAtwnHqNPHI228Y9Dxc1ej4+GeHoNrV9KRV3kIaFlugWi5+BiSS4B1Zvbp1Fvp2y3OINSttY2ZnWdmi8zsMMJ3/5GZnQ7cQrjdo1PpeALYKOmlcdHxhOEIO3o8CEXNYyTNjr9RNR0dPR41xjsGq4G3xtbOY4BtqaJpy8nHw22s25V48SJzEqHV5j+Bj3Ron68hFB3uAe6O00mE+qubgQeAHwLzO3gcjgOuj69fRDgp1wPfBGZ2YP+vBNbEY/JdYF43jgfwj8BvgHuBrxHGgO3I8QCuJNTdDRNyrWeNdwwIjTcXx/P214SW2XamYz2hrqx6vn4xtf5HYjruB07s1Dmbt8lvfXLOFUYeipzOOdcSHtCcc4XhAc05Vxge0JxzheEBzTlXGB7QnHOF4QHNOVcY/x+H+a5xtIeXkgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for gauge_level in [1, 1.5, 2]:\n",
        "    plt.title(f'Water depth image for gauge level {gauge_level}')\n",
        "    plt.imshow(m.infer(gauge_level))\n",
        "    plt.colorbar()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eDaWAcsCbnsL"
      },
      "source": [
        "### Manifold extrapolation example\n",
        "\n",
        "In addition, for unprecedented gauge levels, the model is able to extrapolate according to the DEM. For example, for gauge level of 7, the model floods the pool that exists in the DEM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "executionInfo": {
          "elapsed": 598,
          "status": "ok",
          "timestamp": 1646917312523,
          "user": {
            "displayName": "Yotam Gigi",
            "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiLLtMlSl4qBDGAbejfo_FJcpHnwRWacAtTdaUb=s64",
            "userId": "14733821969979488546"
          },
          "user_tz": -120
        },
        "id": "yd9b6_PTbzQF",
        "outputId": "17322f00-f4a5-4faf-919f-bee62a578707"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 432x288 with 2 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(f'Water depth image for gauge level 7.')\n",
        "plt.imshow(m.infer(7.))\n",
        "plt.colorbar()\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "kgNKRCvtcbmb",
        "jQE8BICTSSuY",
        "0YKWqHLHM4PA"
      ],
      "name": "Inundation Models.ipynb",
      "provenance": [
        {
          "file_id": "1J8NM-PtNQc1X0C3jgmXXbx_PiozyEIkj",
          "timestamp": 1646918017612
        },
        {
          "file_id": "1WUzfyPoHtayU8gOQlbpTLy5t8F9LHqoq",
          "timestamp": 1646916948471
        }
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
